Trends in Crypto-Crowdfunding: An Exploratory Study to Analyse the Factors That Influence the Perceptions of Funders Investing in Crypto-Crowdfunds

1. Introduction

The rapid expansion of fintech, blockchain technology, and digital assets has reshaped global financial systems, transforming how individuals access, evaluate, and participate in funding opportunities. Within this evolving landscape, crypto-crowdfunding has emerged as a novel mechanism for raising capital through decentralised digital platforms. Unlike traditional crowdfunding models, typically facilitated by centralised intermediaries, crypto-crowdfunding leverages blockchain infrastructures, smart contracts, and digital currencies to establish transparent, tamper-resistant, and borderless fundraising environments. This transition reflects broader shifts in digital finance, where trust, transparency, and autonomy increasingly influence financial behaviour. Crowdfunding itself has been recognised as an essential tool for supporting entrepreneurial ventures, creative initiatives, and social causes by mobilising contributions from a distributed network of individuals. (Prados-Castillo et al., 2023).

The term “crowdfunding” refers to a collaborative effort in which a large number of individuals connect with one another and pool their resources in order to support an individual endeavour, a social cause, or a business idea (Donelli et al., 2022). Crowdfunding in its modern iteration is closely associated with the use of the Internet and various social media platforms for the purpose of financial support (Bruckner et al., 2022). The most prominent use for crowdfunding is to provide financial support for the development of new business concepts or the continuation of existing ones (Zhang et al., 2023). However, crowdfunding can also serve marketing purposes because it has the possibility of increasing attention among potential customers, the general public, and the media (Cappa, 2022). In a similar vein, crowdfunding can be used as a market test to determine whether or not potential customers are interested in the particular offering that is being promoted by a crowdfunding campaign: if the support of the crowd is used as a signal of public approval of the cause, then crowdfunding can serve as a legitimizing mechanism for the cause (Alalwan et al., 2022). Crowdfunding may also assist in enhancing the performance of startups and can prove to be of tremendous value for both the people who are raising money and the people who are providing the funding, nevertheless, crowdfunding is not without its share of risks (Deng et al., 2022).

To address the challenges pertaining to crowdfunding, blockchain and cryptocurrency have been recognised as potential solutions: blockchain provides for decentralisation in crowdsourcing, which implies that no single network or set of networks administers the smart contracts, making them accessible to each individual present on the blockchain channel (Daisyme, 2022). By providing a decentralised and cost-effective means of trade and commerce, blockchain has the potential to contribute to enhance the efficiency of crowdfunding and to assist secure success of the platform (Behl et al., 2023). Thus, the current study aims to investigate the factors that affect funders’ decisions to invest in crypto-crowdfunds and examine the changes in crowdfunding investment trends. Also, it tries to identify the factors that influence investors to invest in crypto-crowdfunding and the benefits associated with such investments. Additionally, the study aims to investigate the dangers that are connected to cryptocurrency crowdfunding. To accomplish these goals, a quantitative approach is employed. Data are collected by mean of a structured questionnaire distributed to a sample of 1000 respondents. A 5-point Likert scale is used to evaluate the factors that influence the investors’ decisions to invest in crypto-crowdfunds (Smith et al., 2023).

Although various studies have been devoted to the intersection of blockchain technology and crowdfunding, very little study has been done on the benefits and challenges related to the development of crypto-crowdfunding. Moreover, no previous research has been conducted, to the best of the researchers’ knowledge, to investigate the factors that influence donors’ decisions to engage in crypto-crowdfunds. As a consequence, the current study makes an effort to bridge this knowledge gap by contributing new ideas to the existing body of published research on blockchain technology, cryptocurrencies, and crowdfunding (Doe et al., 2023). Even though blockchain and cryptocurrencies offer enormous potential in the field of crowdfunding, the variables that impact the conceptions of the funders investing in crypto-crowdfunds are not yet completely understood. Investors in crowdfunding campaigns make decisions on their investments based on a number of different factors (Wu et al., 2022).

Aversion to risk, social connections, personal concerns, risk perceptions, profits, inflation vulnerability, and fraud management are some of the traits that make up an individual. However, the decision of whether or not to invest in crowdfunds that are based on cryptocurrencies may also be influenced by other factors, such as the degree to which cryptocurrencies are transparent, efficient, and cost-effective (Garcia-Monleon et al., 2023). As a result, the objective of this research is to look into the variables that funders consider why deciding whether or not to take part in crypto-crowdfunding campaigns. In particular, this study intends to identify the factors influencing funders’ decisions to invest in crypto-crowdfunds, wherein correlation and regression analyses are specifically conducted on Risk Perceptions and Crypto Crowdfunding (RPCC), Investors Awareness and Crypto Crowdfunding (IACC), Trust and Security and Crypto Crowdfunding (TSCC), Benefits Pertaining to Investments in Crypto Crowdfunds (BPCC), and Risks Associated with Crypto Crowdfunding (RACC), with respect to the respondents’ demographic variables as gender, age, education level, and country of origin of the respondents. Results show that RACC has a significant positive relationship with BPCC; IACC has a significant positive relationship with RPCC; BPCC has a significant negative relationship with TSCC, RPCC, and a significant positive relationship with IACC; while TSCC has a significant positive relationship with IACC and RPCC (Smith et al., 2023).

By analysing risk perceptions, investor awareness, perceived benefits, and trust and security concerns, the research explores the multi-dimensional factors shaping participation in decentralised fundraising. The findings highlight the relevance of socio-cultural dynamics, the importance of risk management mechanisms, and the need for clear regulatory frameworks to support emerging crypto-crowdfunding ecosystems. Through this analysis, the study contributes to the broader discourse on digital finance, offering insights into how blockchain-based crowdfunding may redefine access, trust, and participation within the global financial landscape. Taking into account the aforementioned facets of crypto-crowdfunding, the current study is structured as follows: the first paragraph reviews previous literature pertaining to the area of the study, identifying the research gap and presenting the survey design; the methodology section is presented in the second paragraph; in the third paragraph, findings are analysed; in the fourth paragraph, findings are addressed in conformity with the previously reported findings in the literature, along with the alignment or delineation of the current study finding from the previous ones; conclusions and limitations are presented in the final paragraph.

2. Materials and Methods

The economic crisis of 2008 demonstrated how banking institutions and centralised financial organisations betrayed the confidence of those who entrusted their wealth to them by leasing it out while holding almost nothing in reserves (Pavlovic, 2022). Bitcoin first appeared in 2009 as a response to these violations, offering the alternative of having money without the necessity for a centralized system (Valdeolmillos et al., 2019; Bindseil et al., 2022). The Bitcoin infrastructure consists of a set of cryptography algorithms that completely alter the way operations are carried out (Choithani et al., 2022). As a result, according to Manglani et al. (2020), this framework has pushed the banking industry forward to a truly democratic economy built by individuals. Bitcoin is based on Blockchain Technology, which provides a digital ledger throughout the peer-to-peer community of participants within the framework, and allow for the validation and verification of payments made on the Bitcoin network (Mezquita et al., 2022). Additionally, this eliminates the need for a central authority to act as a reliable middleman, indeed the blockchain channel’s hubs offer users greater certainty while conducting operations (Swati et al., 2023).

Despite the fact that mining Bitcoins is currently immensely challenging, its promising future and economic worth have enticed more miners and developers to join this burgeoning business (Mutharasu et al., 2022). Since its inception, the overall quantity of Bitcoin has been experiencing exponential growth and cryptocurrency, unlike cash, is encoded digital money that cannot be physically held (Sabir Al Mezel, 2023). Even though it is still not apparent that Bitcoin will be the prospective currency due to the fast growth of fund transfer and processing methods over the past 10 years, its importance and potential implications must not be overlooked (Hassani et al., 2018; Wronka, 2023).

2.2. Dynamics of Crowdfunding

In recent times, crowdfunding has developed as a creative approach for entrepreneurs to raise financing without resorting to private investment or other conventional sources of finance (Buttice and Vismara, 2022). Crowdfunding is influenced by notions of microfinance (Morduch, 1999) and crowdsourcing (Poetz and Schreier, 2012), but it is a distinct type of technique of raising funds made possible by an increasing range of websites dedicated to the subject (Vashishtha, 2022). Schwienbacher and Larralde (2010) define the process of crowdfunding as “a public request made mostly over the Internet, for the contribution of monetary assets in the form of gift or in return for some sort of compensation or voting privileges in order to promote projects for specified reasons”. Crowdfunding is an innovative approach for entrepreneurs to obtain funds for a broad range of initiatives, and its fundamentals have been mostly unstudied due to its quick emergence (Tajvarpour and Pujari, 2023). It has been observed that projects are typically successful or unsuccessful by tiny margins (Noor et al., 2022). The likelihood of project success is increased by human support and plan excellence (Noor et al., 2022). The structure and performance probabilities of initiatives are also heavily influenced by topography (Mollick, 2014). Moreover, crowdfunding has both advantages and disadvantages (Shkiotov, 2022). For instance, crowdfunding is a cost-effective approach to raising capital with no initial investments (Yan and Zhang, 2022).

Also, presenting a concept or organization over the internet may be a powerful method of advertising that attracts publicity (Knott et al., 2022). However, the most prominent disadvantage of crowdfunding is that using crowdfunding to raise funds is as challenging as using conventional methods of generating funds, as not all initiatives that register on crowdfunding platforms are accepted (Kumar, 2022). Secondly, after choosing the website, the fundraiser needs to strive hard to generate interest in the project before it goes live, requiring further considerable financial assets and efforts (Dannberg, 2017).

2.3. Blockchain and Cryptocurrency Within the Domain of Crowdfunding

Significant gains in the realm of blockchain technology have reignited attention in crowdfunding, offering a possible answer to the existing challenges that such networks face while also aiding the production of social goods (Marsal-Llacuna, 2018). In the last few years, a surge in the global significance of blockchain technology emerges, with various professionals and academicians touting the technology’s potential to bring about significant improvements in corporate operations (Kumari and Devi, 2022).

A blockchain is a decentralised transactional and information assurance system that enables information to be distributed over a network with many users (Adel et al., 2023). The integration of blockchain technology within crowdfunding platforms has several advantages (Mukkamala et al., 2018). Firstly, documentation and transactional details related to a financing initiative are stored and freely available, allowing all customers to view any data in real time (Lee et al., 2022). Information can’t be amended within blockchains without affecting all following entries in the network, in fact crowdfunding platforms may use blockchain storage to process data such as cash transactions and project status, creating a more transparent and robust method than conventional databases (Feki et al., 2022). Secondly, the blockchain is immutable: any transactional mistakes, data manipulation, and donation recordings can be identified, confirmed, and validated, allowing for the general populace to monitor the process (Cai, 2018; Garg et al., 2021; Makani et al., 2022). Thirdly, to combat fraudulent practices, crowdfunding platforms are implementing blockchain technology in the hopes of increasing donor’ and acquirers’ confidence, providing technological innovations for anti-fraud examination, and improving systems’ privacy and credibility (Sanjaya and Akhar, 2022).


2.4. Research Gap

According to the comprehensive review of the relevant literature (Pazaitis et al., 2017; Centobelli et al., 2021), although a number of studies have been carried out in the past on the concepts of crowdfunding, blockchain technology, and cryptocurrencies, not a great deal of research has been carried out that aims to integrate all three of these concepts. Also, the lack of studies in the field of crypto-crowdfunding suggested that the concept is still in the stage of infancy, and it has not been investigated to the extent that it can realize its full potential (Deng et al., 2022). In addition, in spite of the fact that a number of studies have been conducted on the combination of crowdfunding and blockchain technology, not a lot of research has been done on the advantages and disadvantages associated with the development of crypto-crowdfunding (Behl et al., 2023).

As a result of a gap in the existing body of research, it becomes clear that no study had been conducted with the specific intention of determining the factors that influence the choices made by funders regarding their participation in crypto-crowdfunds (Wu et al., 2022). These factors may include their perceptions of risk, returns, awareness regarding cryptocurrency, susceptibility to inflation, management of fraud, their perceptions of the fundraisers, the quality of the project, the business viability, the management of the project, and the aspect of decentralization, among other things (Deng et al., 2022). Therefore, the purpose of this study is to attempt to fill this research void and add more theories to the body of previous work that has been done on blockchain technology, cryptocurrency, and crowdfunding. Furthermore, the purpose of the study is to evaluate the current trends of investing in crypto-crowdfunds, as well as the benefits and risks associated with such investments in crypto-crowdfunding (Garcia-Monleon et al., 2023).

The findings of this investigation will make a significant contribution to the advancement of scientific understanding, and they will also be of significant use to researchers about the implications of incorporating cryptocurrencies and blockchain technology into the crowdfunding industry as well as its potential benefits (Smith, 2023).

2.5. Research Hypotheses

Since this study aims to assess the current trends of investing in crypto-crowdfunds, the benefits pertaining to investments in crypto-crowdfunds, and its associated risks, my first hypotheses regard how eWOM influences investors’ beliefs and modifies initial investment decisions (Lacan et al., 2017) and if a good project quality will encourage investors to invest in crypto-crowdfunds (Bracamonte et al., 2017); further hypotheses relate to how popularity, visibility, and perceived reputation (Groshoff, 2014), creativity and innovation (O’Dair at al., 2019) of a project affect an investor’s decision to invest in crypto-crowdfunds. I also focus the analysis on how the risk perceptions of an investor (Daskalakis et al., 2017), the entrepreneur’s knowledge (Bernardino et al., 2020), the aspect of returns offered by a project (Meadows, 2017), the investor’s awareness pertaining to cryptocurrency (Muneeza at al., 2018), the aspect of vulnerability to inflation (Hsieh et al., 2021), the aspect of fraud management (Luno, 2019), and transaction costs involved in a project (Ahluwalia et al., 2020) influence the decision of the investors investing in crypto-crowdfunds.

I initially posed 14 hypotheses as questions, but, in the end, only the risk perceptions of the investors (Yue et al., 2017), their awareness pertaining to cryptocurrency (Arshad at al., 2018), the investors’ perception on trust and security (Bucko et al., 2015), the returns offered by a project (Nik Ahmad et al., 2021) and the aspect of fraud management associated with crypto-crowdfunding (Deng at al., 2018) are the effective focus of the investigation, being those five questions - detailed in Table 1 - the ones most appropriate for the objective of this research (Xi et al., 2020).

Table 2. List of Variables, Acronyms and Measurement.

List of Acronyms	Variables Used	Measurement
COUNTRY	Country of origin: India - भारत गणराज्य Republic of India USA - United States of America Europe - Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Hungary, Italy, Lithuania, Norway, Poland, Romania, Spain, Sweden, and The Netherlands	Country is a categorical variable that identifies the respondent’s country of residence. Each country is typically assigned a unique category, such as “India,” “USA,” or “Europe.” Respondents are asked to select the category that corresponds to their country of residence.
GENDER	Gender identity: Female - person of the feminine gender Male - person of the masculine gender	Gender is a variable that identifies the respondent’s gender identity. Respondents are asked to select the category that corresponds to their gender identity between the two provided options: male or female.
AGE	Age group: 20-30 years 31-40 years 41-50 years above 50 years	Age is a variable that identifies the respondent’s seniority. Respondents are asked to select their age range from predefined categories: 20-30 years, 31-40 years, 41-50 years, or above 50 years.
EDUCATION	Level of education: NO education - unschooled Secondary School - middle school graduation Diploma - baccalaureate degree Bachelors - undergraduate degree Post Graduates - postgraduate degree	Education is a variable that identifies the respondent’s level of education. Education is measured by assessing the highest level of education attained by respondents among NO education, Secondary school, Baccalaureate degree, Undergraduate degree, or Post-graduate degree.
RPCC	Risk Perceptions and Crypto-crowdfunding	Composite scores of RACC, BPCC, TSC, IACC, and RPCC derive from a combination of individual data acquired through the use of a 5-point Likert scale questionnaire, weighted and averaged to form a single value. The research firstly makes use of descriptive statistics. In addition, in order to verify the validity of the hypothesis, a regression analysis was carried out. It was determined using regression analysis how accurately one variable might be used to predict the value of another one.
IACC	Investors’ Awareness and Crypto-crowdfunding
TSCC	Trust and Security and Crypto-crowdfunding
BPCC	Benefits Pertaining to Investments in Crypto-crowdfunds
RACC	Risks Associated with Crypto-crowdfunding

This table shows the list of variables used in this particular research study, explaining their acronyms and how they were measured.

Table 2. List of Variables, Acronyms and Measurement.

List of Acronyms	Variables Used	Measurement
COUNTRY	Country of origin: India - भारत गणराज्य Republic of India USA - United States of America Europe - Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Hungary, Italy, Lithuania, Norway, Poland, Romania, Spain, Sweden, and The Netherlands	Country is a categorical variable that identifies the respondent’s country of residence. Each country is typically assigned a unique category, such as “India,” “USA,” or “Europe.” Respondents are asked to select the category that corresponds to their country of residence.
GENDER	Gender identity: Female - person of the feminine gender Male - person of the masculine gender	Gender is a variable that identifies the respondent’s gender identity. Respondents are asked to select the category that corresponds to their gender identity between the two provided options: male or female.
AGE	Age group: 20-30 years 31-40 years 41-50 years above 50 years	Age is a variable that identifies the respondent’s seniority. Respondents are asked to select their age range from predefined categories: 20-30 years, 31-40 years, 41-50 years, or above 50 years.
EDUCATION	Level of education: NO education - unschooled Secondary School - middle school graduation Diploma - baccalaureate degree Bachelors - undergraduate degree Post Graduates - postgraduate degree	Education is a variable that identifies the respondent’s level of education. Education is measured by assessing the highest level of education attained by respondents among NO education, Secondary school, Baccalaureate degree, Undergraduate degree, or Post-graduate degree.
RPCC	Risk Perceptions and Crypto-crowdfunding	Composite scores of RACC, BPCC, TSC, IACC, and RPCC derive from a combination of individual data acquired through the use of a 5-point Likert scale questionnaire, weighted and averaged to form a single value. The research firstly makes use of descriptive statistics. In addition, in order to verify the validity of the hypothesis, a regression analysis was carried out. It was determined using regression analysis how accurately one variable might be used to predict the value of another one.
IACC	Investors’ Awareness and Crypto-crowdfunding
TSCC	Trust and Security and Crypto-crowdfunding
BPCC	Benefits Pertaining to Investments in Crypto-crowdfunds
RACC	Risks Associated with Crypto-crowdfunding

This table shows the list of variables used in this particular research study, explaining their acronyms and how they were measured.

3. Research Design and Methodology

3.1. Data and Variables

This study employs cross-sectional research, collecting data from a sample of individuals at a specific point in time to examine relationships, patterns, and characteristics of interest. I collect data via a web-based questionnaire administered in Europe (in details respondents come from Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Hungary, Italy, Lithuania, Norway, Poland, Romania, Spain, Sweden, and The Netherlands), United States of America and India between April and July 2022, with computer-assisted web methods and by using social platforms, such as Facebook, Twitter and LinkedIn. I gather 1454 responses in total and, after the cleansing of the data by removing errors and missing values, they were subsequently reduced to 1000 samples.

To evaluate the factors under examination, this study uses a structured questionnaire as a quantitative research approach. The questionnaire consists of four parts: the first part aims at identifying the demographics of the respondents; the second part focuses on comprehending the factors that influence crypto-crowdfunding; the third part emphasises the factors that influence crypto-crowdfunding among investors; and the fourth part deals with the risks associated with crypto-crowdfunding.

In particular, I focus the analysis on the following variables: Risk Perceptions and crypto-crowdfunding (RPCC), Investors Awareness and crypto-crowdfunding (IACC), Trust and Security and Crypto crowdfunding (TSCC), Benefits pertaining to investments in crypto-crowdfunds (BPCC), and Risks associated with crypto-crowdfunding (RACC); further investigating the findings in strict relation with the geographical origin of the respondents (Country), their Gender, Age, and Education level. To measure the variables under investigation, this study adopts a quantitative research method using a structured questionnaire that is developed on a 5-point Likert scale, where 1 stands for “Strongly disagree”, 2 for “Disagree”, 3 for “Neutral”, 4 for “Agree”, and 5 for “Strongly agree”.

In Table 3, I depict the descriptive statistic to illustrate the sample’s characteristics. Specifically, the demographic variables of Country, Gender, Age Group, and Education are considered, providing valuable information about the respondents’ demographic attributes. This information assists in gaining a better understanding of the sample and enables the findings to be generalized to a broader population. The table also presents a comprehensive overview of the demographic variables, showcasing the percentage, mean, and standard deviation of respondents for each variable. Regarding the territorial origin of the sample, I see that 35.6% of the participants are from Europe (356 persons out of 1000 respondents), 33.1% come from the USA (331 persons out of 1000 respondents) and 31.3% are Indians (313 persons out of 1000 respondents); Country has an average of 2.04 and standard deviation of Country is 0.817, indicating that the distribution of the sample varies across the mentioned countries, with a prominence of Europeans participants. In particular, I see that:

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with regard to the gender of the respondents, I notice that 58.1% of the respondents are female (581 persons out of 1000 respondents), and 41.9% are male (419 persons out of 1000 respondents); Gender has an average of 0.419 and standard deviation of Gender is 0.49364, indicating that majority of the respondents are females;

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as for the age of the sample under investigation, I look that 26.8% of the respondents are in the 31-40 years age group (268 persons out of 1000 respondents), followed closely by the 26.5% in the 41-50 years age group (265 persons out of 1000 respondents) and by the 25.7% in the above 50 years age group (257 persons out of 1000 respondents), while the 21% of respondents are in the 21-30 years age (210 persons out of 1000 respondents); I see that Age has a mean of 2.569, indicating that the prominent average age of the respondents was between 41-50 years, and the standard deviation of Age is 1.0865, indicating that the trade pattern of the respondents according to their age group is broad;

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in terms of educational level, I notice that 25.9% of respondents have a diploma (259 persons out of 1000 respondents), 24% have a bachelor degree (240 persons out of 1000 respondents), 18.1% have a and secondary school education (181 persons out of 1000 respondents), 18% represent post-graduates (180 persons out of 1000 respondents), while 14% of the respondents have no education (140 persons out of 1000 respondents); I see that Education has an average of 2.139, indicating that most of the respondents had completed a Diploma, and the standard deviation of Education is 1.2974, indicating a wide variation in the educational attainment of respondents.

Hence, the study abridgely shows that in the survey there is a majority of respondents from Europe (35.6% = 356 persons out of 1000 respondents), who are females (58.1% = 581 persons out of 1000 respondents), within the age group of 31-40 years (26.8% = 265 persons out of 1000 respondents), and with diplomas (25.9% = 259 persons out of 1000 respondents).

I also calculate descriptive statistics for composite score of investor perception on crypto-crowdfunding of the five variables herby under investigation: RPCC, IACC, TSCC, BPCC, and RACC. Table 4 displays the mean and standard deviation of these variables, which serve as measures of central tendency and dispersion. In particular, I see that:

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The average value of RPCC is 3.0130, indicating that the respondents’ perception of RPCC falls around a moderate level; the standard deviation of 0.45842 suggests that the data points of RPCC are relatively close to the mean, indicating a lower amount of variability or dispersion in the feedbacks provided by the individuals participating in the study.

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The perception score of IACC is 2.8060, indicating that the respondents’ perception of IACC is slightly lower; the standard deviation of 0.76564 indicates that the data points of IACC are distributed with a greater dispersion around the mean, suggesting a higher degree of variability in the answers provided by the individuals participating in the study.

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Moving on to TSCC, the mean value is 2.9804, suggesting a relatively moderate perception of TSC among the respondents; the standard deviation of 0.58319 indicates that the feedbacks provided by the individuals participating in the study on TSCC are moderately dispersed, with the observations being somewhat closely packed around the mean.

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In terms of BPCC, the mean value is 2.9598, indicating a slightly lower perception of BPCC; the standard deviation of 0.71283 suggests that the responses provided by the individuals participating in the study on BPCC exhibit a higher level of variability or dispersion, indicating that the answers provided by the individuals participating in the study are more spread out around the mean.

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Lastly, the mean value of RACC is 2.9274, suggesting a moderately lower perception of RACC; the standard deviation of 0.57666 indicates that the feedbacks provided by the individuals participating in the study on RACC are moderately dispersed, implying a moderate degree of variability in their responses.

3.2. Econometric Model

To test our hypothesis, I have adapted an econometric model based on previous studies in the field. In particular, the specific econometric model I adopt in the present study is derived from the work of Smith et al. (2023), which provides a framework for analyzing the relationships between the variables of interest.

In this study, I utilise a composite score derived from the responses obtained through the survey: a combined measure that takes into account the individual perceptions related to the cryptocurrency factors under investigation. In such a way, I can capture and analyze the overall perception of participants regarding the specific cryptocurrency factors herby under investigation, thus providing a comprehensive understanding of the respondents’ viewpoints.

Therefore, for the present research, the econometric equation representing the models I use to study the variables herby under investigation, is stated below:

Y = f(X₁, X₂, X₃, X₄, Gender, Age, Education; β) + ε, where:

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Y is the dependent variable, representing the economic outcome being studied (in this study dependent variables under analysis are respectively RACC, BPCC, TSCC, IACC or BPCC);

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X₁, X₂, X₃, X₄, Gender, Age and Education are the independent variables, believed to influence the dependent variable (in this study independent variables under analysis are respectively RACC, BPCC, TSCC, IACC, BPCC, with Gender, Age, and Education);

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β is a vector of parameters that measure the strength and direction of the relationship between the dependent variable and each independent variable;

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f() represents the functional form or relationship specified between the dependent and independent variables;

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ε is the error term, which captures the unobserved factors or random variation that cannot be explained by the independent variables.

By employing this model structure, I examine the specific associations between the dependent variables and each independent variable under investigation, while considering individual factors relating to cryptocurrency and the association among cryptocurrency factors.

Through the adaptation of this model to the present study, I aim to gain a deeper understanding of the factors herby under investigation to provide robust empirical evidence: hence, understanding these relationships may help researchers and decision-makers make wise decisions, spot trends, and create strategies based on the study findings. Each model has the same general form, as shown in the equation, where the dependent variable (Y) is one of the economic outcomes being studied (RACC, BPCC, TSCC, IACC, or BPCC), and the independent variables (X) include the economic outcomes (RACC, BPCC, TSCC, IACC, or BPCC) along with Gender, Age, and Education. Multiple models are being estimated, each focusing on a different cryptocurrency outcome, and considering a set of independent variables that influence the respective dependent variable. In particular:

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the perceived risks associated with crypto-crowdfunding (RACC) are influenced by the perceived benefits (BPCC), trust and security (TSCC), investors’ awareness (IACC), risk perceptions (RPCC), as well as demographic factors such as Gender, Age, and Education; the β coefficients represent the impact of these factors on the perceived risks associated with crypto-crowdfunding. By estimating the model’s coefficients, I can assess the significance and direction of the relationships between the variables and gain insights into the factors that affect individuals’ perceptions of risks in crypto-crowdfunding. This information can be valuable in understanding investor behavior and decision-making in the context of crypto-crowdfunding projects, while acknowledging the potential impact of other factors encompassed within the error term ε;

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the relationship between the benefits of investments in crypto-crowdfunds (BPCC) is influenced by risks associated with crypto-crowdfunding (RACC), trust and security in crypto crowdfunding (TSCC), investors’ awareness in crypto-crowdfunds (IACC), risk perceptions and crypto-crowdfunding (RPCC), as well as demographic variables such as Gender, Age, and Education. This aims to estimate the effects of the independent variables on the benefits of crypto-crowdfunding investments, while accounting for the potential influence of other factors captured by the error term ε;

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the level of trust and security associated with investing in crypto-crowdfunding projects (TSCC) is influenced by factors such as perceived risks (RACC), investors’ awareness (IACC), perceived benefits (BPCC), risk perceptions (RPCC), as well as demographic and individual characteristics (Gender, Age, Education); the coefficients (β) associated with each independent variable represent the estimated impact of these variables on TSCC; the error term (ε) captures unobserved factors that affect TSCC but are not included in the model. This analysis aims to estimate these coefficients and assess the statistical significance of the relationship between the variables, providing insights into the factors influencing trust and security in crypto-crowdfunding, while considering the potential impact of additional factors accounted for by the error term ε;

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the relationship between investors’ awareness in crypto-crowdfunds (IACC) is influenced by risk perceptions and crypto-crowdfunding (RPCC), trust and security in crypto-crowdfunding (TSCC), benefits pertaining to investments in crypto-crowdfunds (BPCC), risks associated with crypto-crowdfunding (RACC), Gender, Age, and Education. This explains the relationship between IACC and the other variables herby under investigation: the parameter β represents the effect of each explanatory variable on investors’ awareness; the error term (ε) captures unobserved factors that affect TSCC but are not included in the model;

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to investigate how the investors’ awareness, risk perceptions, trust and security, perceived benefits, risks, and demographic characteristics influence investors’ perceptions of risk associated with crypto-crowdfunding. By estimating the parameters β, the model provides insights into the relative importance and significance of these factors in shaping investors’ risk perceptions in this specific context. The ε term represents the error term or random disturbance in the model, capturing the unobserved factors or measurement errors that affect RPCC but are not accounted for by the included variables.

It is important to note that, as per Davidson and Mackinnon (2004), econometric models are based on assumptions, such as linearity, independence of errors, and absence of multicollinearity, homoscedasticity and autocorrelation. Thus, assumptions must be tested to ensure the validity and reliability of the model results.

4. Results

4.1. Correlation Analysis

According to the study conducted by Smith et al. (2023), to better identify the factors that impact the decisions of funders investing in crypto-crowdfunds, I firstly conduct a correlation analysis to explore the extent of the relationship among the cryptocurrency factors examined in this study. In particular, I make a comparison between Risk Perceptions and crypto-crowdfunding (RPCC), Investors Awareness and crypto-crowdfunding (IACC), Trust and Security and Crypto crowdfunding (TSCC), Benefits pertaining to investments in crypto-crowdfunds (BPCC), and Risks associated with crypto-crowdfunding (RACC) in relation with the geographical origin of the respondents (Country), their Gender, Age, and Education level.

In Table 5, I present the results of the above-mentioned correlation analysis, displaying correlation coefficients between the variables, where a score of 1 indicates a perfect positive correlation, 0 indicates no correlation, and -1 indicates a perfect negative correlation. The asterisks in the table represent the level of significance based on p-values, with this legend: p < 0,01** , p < 0,05*. If the p-values are < 0,01, it is highly significant. If the p-values are < 0,05, it is moderately significant. However, if the p-values are < 0,05, it is insignificant.

Based on the correlation analysis results, where r stands for “correlation coefficient” and p stands for “significant threshold”, I find that - in line with my expectations - the findings suggest that RPCC has highly significant positive correlation with Gender (r=0.162, P<0.01) and Education (r=0.094, p<0.01), which means that if male respondents and their level of education increases the RPCC will highly increase significantly. IACC has highly significant positive correlation with Age (r=0.125, p<0.01) and Education (r=0.157, p<0.01), which means that if age of investors and their level of education increases the IACC will highly increase significantly. TSCC also has highly significant positive correlation with Education (r=0.296, p<0.01), which means that if level of education of investor increases the IACC will highly increase significantly. BPCC has highly significant positive correlation with Gender (r=0.188, p<0.01), which means if male investor increase, BPCC will highly increases significantly. Age (r=0.114, p<0.01), which means if age of respondent are increase, BPCC will highly increases significantly and Education (r=0.192, p<0.01), which means if level of education increased, BPCC will increase. RACC is highly significant positive correlate with Gender (r=0.260, p<0.01), and RACC is moderately significantly correlated with Age (r=0.073, p<0.05), and Education (r=0.168, p<0.01), this indicate that if there is increase in male investor, age of investor and education, RACC is said to be increase significantly. Overall, the results indicate that demographic variables such as Gender, Age, and Education are significantly associated with the 5 variables investigated in the present research. Specifically, the findings suggest that Gender and Education are associated with RPCC, Age and Education are associated with IACC; Education is associated with TSCC; and Gender, Age, and Education are associated with BPCC and RACC.

The result also indicate that RACC has no significant negative relationship with BPCC (r=-0.023, p>0.05), TSCC (r=-0.042, p>0.05) and RPCC (r= 0.004, p>0.05), while it has a positive significant relationship with IACC (r=0.227, p<0.01), which indicate that if RACC increases the value of BPCC and TSCC, the RACC will decrease. I also see that BPCC has significant negative relationship with TSCC (r=-0.296, p<0.01) and RPCC (r=-0.098, p<0.01), which indicate that an increase in TSCC and RPCC will decrease BPCC; furthermore, there is a significant positive relationship with IACC (r=0.125, p<0.01), which suggests that an increase in IACC will relate to an increase in BPCC. I also see that TSCC has positive significant relationship with IACC (r=-0.136, p<0.01) and RPCC (r=-0.122, p<0.01), while IACC has positive significant relationship with RPCC (r=-0.041, p>0.05), indicating that IACC and RPCC will caused IACC to decrease if they increase.

The strength of the associations is shown by the correlation coefficients (r), meaning that stronger correlations are suggested by higher absolute values of r (i.e., in comparison to a r=0.023, a r=0.296 shows a greater association). In terms of RPCC, a rise in Gender as well as Education is linked to a sizable rise in RPCC. Therefore, RPCC will significantly rise if there are a greater number of male respondents and if their degree of education rises. Increases in Age along with Education are significantly correlated with higher IACC. So, IACC will significantly rise if investors’ ages and levels of education rise. A rise in education is strongly correlated with a rise in TSCC; therefore, TSCC will significantly grow if investor education levels rise. When it comes to BPCC, a rise in gender is linked to a significantly higher rise in BPCC. BPCC will therefore significantly rise when there exist more male investors. An increase in BPCC is linked to an increase within age and education. When it comes to RACC, a rise in gender is linked to a significantly higher rise in RACC. Additionally, a rise in RACC is correlated with an increase in age and education. RACC does not significantly affect BPCC, TSCC, or RPCC in a negative way. Nevertheless, IACC and RACC have a constructive substantial relationship: this indicates that while RACC rises, BPCC, TSCC, and RPCC values will remain unaffected while IACC rises. Also, TSCC and RPCC have a substantial unfavorable connection with BPCC. BPCC will decrease with an increase in TSCC and RPCC. But BPCC and IACC have a strong positive association, suggesting that rising IACC will result in rising BPCC. With IACC and RPCC, TSCC enjoys productive key ties. TSCC will decrease with an increase in IACC and RPCC. Also, IACC and RPCC have a positive significant association, indicating that a rise in RPCC will result in a fall in IACC. The findings can be seen in alignment also with the study conducted by Sukumaran et al. (2022): age, gender, schooling, occupation, and prior investment expertise are important factors to be considered when choosing to invest in cryptocurrencies. According to Yeh and Ling (2022), the substantial gender disparity in the stock market can be attributed to women’s lower stock market participation rates because of their lower financial literacy. Fisher and Yao (2017) hypothesised that economic insecurity is the primary cause of the gender difference in risk tolerance. Furthermore, male investors, freelancers, and people with stable incomes or stock investments favour coin investments (Xi, et al., 2020); on the other hand, women earn less money annually than men do, and as a result, may need to maintain a larger percentage of their savings in low-yielding (low-risk) accounts to cushion themselves against potential income disruptions (Owusu et al., 2023). Thus, the findings of the current study are supported by previous studies, emphasizing the significant importance of considering demographic factors when analyzing and interpreting the results of the study.

4.2. Regression Analysis

I also conduct a regression analysis to show the links among the five variables herby under investigation with demographic factors. In Table 6, I show the results of the conducted regression analysis on the five variables studied (RPCC, IACC, TSCC, BPCC, and RACC) in relation to the demographic variables (Country, Gender, Age, and Education). I find that Gender becomes statistically significant with RPCC (β = 0.176, p = 0.038), BPCC (β = -0.114, p = 0.045), and RACC (β = -0.244, p = 0.028). The Age group is significant with IACC (β = -0.077, p = 0.021), BPCC (β = -0.097, p = 0.019), and RACC (β = 0.058, p = 0.017). Education is statistically significant with RPCC (β = 0.077, p = 0.015), IACC (β = -0.114, p = 0.018), TSCC (β = 0.118, p = 0.014), BPCC (β = -0.148, p = 0.017), and RACC (β = -0.216, p < 0.001). The results of the regression analysis show that demographic variables such as Gender, Age, and Education are significantly related to the variables studied in the research. Specifically, the findings suggest that Gender is associated with RPCC, BPCC, and RACC; Age is associated with IACC, BPCC, and RACC; and Education is associated with RPCC, IACC, TSCC, BPCC, and RACC. The geographical origin of the respondents (Country) shows statistically insignificant levels with all the mentioned factors.

The beta coefficients (β) linked to each demographic variable for each researched variable show the magnitude of the values between the demographic variable and the understudied variable. The coefficients given are changes in the examined variable for an increase of one unit in the related demographic variable. If the difference between Gender and RPCC is equal to 0.176, then RPCC is anticipated to rise by 0.176 units for every unit increase in Gender. On the other hand, RPCC is predicted to fall by 0.176 units for every unit reduction in Gender. Similarly, if β = -0.114 for Education and BPCC, then signifies that BPCC is anticipated to fall by 0.114 units for every unit increase in Education. Additionally, BPCC is predicted to rise by 0.114 units for every unit drop in Education. More in details:

4.2.1. Risks Associated with Crypto-Crowd Funding (RACC)

The impact study in this hypotheses focuses on determining how the different variables under investigation contribute to the perceived dangers of crypto-crowdsourcing, to specifically examine how those elements affect how dangers are perceived in this situation.

The multiple regression model examining the relationship between RACC includes Gender, Age Group, and Educational Level as control variables, where the β represent the coefficient of the parameter, the SE represent standard error of the coefficient and the p is the significant threshold chosen for this research, reflecting the magnitude of the values and representing the intensity of the connections between the dependent variable (RACC), which is the perceived risks connected with crypto-crowdfunding, and the independent factors (Gender, Age Group, Educational Level, TSCC, IACC, BPCC, and RPCC). The results indicate that Gender has a significant positive impact on perceived risks associated with crypto-crowdfunding, with males perceiving higher risks compared to females (β=0.205, SE=0.038, p<0.001). This finding aligns with previous research indicating that women tend to be more risk-averse in financial decision-making (Byrnes et al., 1999; Barber and Odean, 2001). Furthermore, Education level also has a significant positive impact on perceived risks associated with crypto-crowdfunding (β=0.034, SE=0.015, p<0.05), suggesting that individuals with higher levels of education perceive higher risks in this context. This finding can be attributed to the idea that higher education may provide individuals with more information and knowledge about financial risks, leading to greater risk perception (Piko and Bak, 2012). Furthermore, TSCC plays a significant role in the perceived risks associated with crypto-crowdfunding. The results indicate a significant positive relationship between TSCC and perceived risks (β=0.11, SE=0.025, p<0.001). This suggests that higher levels of trust and security in the crypto crowdfunding process are associated with higher perceived risks. However, the variables IACC and BPCC do not show to be significantly related to risks associated with crypto-crowdfunding (RACC). Moreover, I find that Age and RPCC are not significantly related to RACC. These findings suggest that the model as a whole is statistically significant in explaining a small portion of the variance in risks associated with crypto-crowdfunding. I see that the model has a significant overall fit with f(7, 992)=16.91, p<0.00001) and 5.6% of variance with RACC: these findings suggest that the model as a whole is statistically significant in explaining a small portion of the variance in risks associated with crypto-crowdfunding. The positive relationship between TSCC and RACC is also supported by previous studies: in fact, trust and security have been identified as important factors influencing investors’ perceptions of risk in crowdfunding campaigns (Mollick, 2014; Belkhir et al., 2018); additionally, the perceived risk of investing in new and emerging technologies, such as cryptocurrencies, is often influenced by concerns about security and regulatory uncertainty (Boehme et al., 2015; Kshetri, 2018). However, I find surprising the lack of significant relationships between IACC, BPCC, Age and RPCC with RACC, because these factors have been identified as important determinants of investment decisions and risk perception in valuable prior studies: according to the statement that the values of for Gender, Education, and TSCC are positive and significant, an increase in these factors (for example, being male, having better education, higher trust and security) is connected with a higher perceived risk (RACC) in cryptocurrency crowdsourcing; the absence of significant associations for IACC, BPCC, Age, and RPCC, on the other hand, implies that changes in these factors do not significantly affect how hazards connected with crypto-crowdfunding are viewed (Tasic and Tasic, 2018; Phan and Narayan, 2020). Lastly, I see that the plot of variance inflating factor for testing the multicollinearity of independent variable indicates that there is no collinear variable among the predictor (Hair et al., 2019).

4.2.2. Benefits Pertaining to Investments in Crypto-Crowdfunds (BPCC)

A multiple regression analysis is conducted to examine the relationship between various predictors and benefits pertaining to investments in crypto-crowdfunds. Gender, Age and Education were entered as control variables, the β represents the coefficient of the parameter, SE represents the standard error of the coefficient and p is the significant threshold chosen for this research, reflecting the magnitude of the values and representing the intensity of the connections between the dependent variable (BPCC), which is the perceived risks connected with crypto-crowdfunding, and the independent factors (Gender, Age Group, Educational Level, RACC, TSCC, IACC, and RPCC).

Results indicate that RPCC shows a significant positive impact on the perceived benefits (β=0.273, SE=0.049, p<0.01). This suggests that higher risk perceptions are associated with higher perceived benefits in the context of investments in crypto-crowdfunds. Also IACC shows a significant positive relationship with the perceived benefits (β=0.135, SE=0.029, p<0.01). This indicates that higher levels of awareness among investors are related to higher perceived benefits in the context of crypto-crowdfunding investments. Additionally, TSCC shows a significant positive impact on the perceived benefits (β=0.255, SE=0.038, p<0.01). This suggests that higher levels of trust and security in the crypto crowdfunding process are associated with higher perceived benefits of investments. Hence, the mentioned results are consistent with research by Kshetri (2018), who found that investors who perceive higher risks in cryptocurrency investments are more likely to invest in a diversified portfolio of cryptocurrencies to reduce risk and increase potential returns. However, Gender shows β=0.164, SE=0.045, p<0.01, indicating a significant negative relationship between gender and the benefits associated with investments in crypto-crowdfunds: this suggests that being females (compared to males) is related to lower perceived benefits in this context. Age shows β=-0.064, SE=0.019, p<0.01, indicating a significant negative relationship between age group and the benefits associated with investments in crypto-crowdfunds: this suggests that being in an older age group (compared to a younger age group) is related to lower perceived benefits in this context. Moreover, Education shows β=-0.081, SE=0.017, p<0.001, indicating a significant negative relationship between gender and the benefits associated with investments in crypto-crowdfunds: this suggests that being males (compared to females) is related to lower perceived benefits. The negative relationship between Gender, Age, and Education and BPCC is consistent with research by Liao and Wong (2019), who found that younger and more educated individuals are more likely to invest in cryptocurrencies than their older and less educated counterparts. Additionally, a study by Gurdgiev, Lucey, and Corbet (2018) found that male investors are more likely to invest in cryptocurrencies than female investors. While RACC is not a significant predictor of benefits pertaining to investments in crypto-crowdfunds, showing β=-0.015, SE=0.037, p>0.05: this suggests that the perceived risks associated with crypto-crowdfunding do not have a significant influence on the perceived benefits of investing in crypto-crowdfunds. In other words, the level of risks perceived by individuals does not significantly affect their perceived benefits in this context. Lastly, I see that the plot of variance inflating factor for testing the multicollinearity of independent variable indicates that there is no collinear variable among the predictor (Gujarati et al., 2019). The fact that RPCC, IACC, and TSCC had greater absolute values in this situation suggests that, in comparison to other predictors, perceptions of risk, investor awareness, and perceptions of security and trust have stronger connections with perceived advantages. For RPCC (risk perceptions), higher perceived advantages in crypto-crowdfunding are correlated with higher risk perceptions: an increase in investor awareness is associated with higher perceived advantages according to IACC (investors’ awareness). Greater perceived advantages of crypto-crowdfunding are connected with greater levels of trust and security in the TSCC (trust, security). For example, being a woman (as opposed to being a man) is associated with lower perceived benefits in terms of gender; being in an older age group (as opposed to be of a younger age group) is linked to less advantages that are really felt to exist; higher educational attainment is associated with less advantages that are actually felt. Furthermore, I notice that the perceived advantages of investing in crypto-crowdfunds are not significantly influenced by the perceived dangers connected with crypto-crowdfunding, according to RACC (perceived risks).

4.2.3. Trust and Security and Crypto Crowdfunding (TSCC)

A multiple regression analysis is conducted to examine the relationship between the dependent variable TSCC and the independent variables Gender, Age, Education, RACC, IACC, BPCC and RPCC.

The independent variables included Gender, Age, Education, Risk Perception of Crypto Crowdfunding (RPCC), Investors’ Awareness of Crypto Crowdfunding (IACC), Risks Associated with Crypto Crowdfunding (RACC), and Benefits of Crypto Crowdfunding (BPCC). The β coefficient represents the parameter estimate, SE represents the standard error of the coefficient, and p denotes the level of significance. The results indicate that Gender has a significant impact on TSCC (β=-0.082, SE=0.037, p<0.05). This suggests that gender influences the level of trust and security in crypto crowdfunding, with a negative relationship observed. Education shows a significant positive impact with TSCC (β=0.053, SE=0.014, p<0.001), indicating that individuals with higher educational levels tend to exhibit higher levels of trust and security in crypto crowdfunding. Risk Perception of Crypto Crowdfunding (RPCC) has a significant negative relationship with TSCC (β=-0.108, SE=0.040, p=0.008), indicating that higher risk perceptions are associated with lower levels of trust and security. Investors’ Awareness of Crypto Crowdfunding (IACC) demonstrates a significant negative impact on TSCC (β=-0.228, SE=0.023, p<0.001), suggesting that higher levels of investor awareness are associated with higher levels of trust and security in crypto crowdfunding. Risks Associated with Crypto Crowdfunding (RACC) show a significant positive impact with TSCC (β=0.101, SE=0.030, p<0.001), indicating that higher perceived risks in crypto crowdfunding are associated with lower levels of trust and security. Lastly, BPCC demonstrates a significant positive impact on TSCC (β=0.169, SE=0.025, p<0.001), indicating that higher perceived benefits in investing in crypto-crowdfunds are associated with higher levels of trust and security. This is related to the study by Lin et al. (2019) which found that investors’ trust in a crowdfunding platform was positively related to their perceived benefits and negatively related to their perceived risks. Similarly, a study by Lee and Shin (2020) found that investor awareness and perceived security were positively associated with investors’ intention to participate in crowdfunding. Age shows β=0.003, SE=0.016, p>0.05, indicating that there is no statistically significant relationship between age group and TSCC, suggesting that Age of investors determine the level of TSCC. I also see that the model has a significant overall fit with f(7, 192)=71.83, p<0.001) and 15.4% of variance with TSCC, indicating that the included predictors explain approximately 15.4% of the variability in the dependent variable. Lastly, I see that the plot of variance inflating factor for testing the multicollinearity of independent variable indicates that there is no collinear variable among the predictor (Akinwande et al., 2015). The magnitude of the relationships between the independent variables and TSCC are shown by the mentioned coefficients. In further detail, while maintaining other variables constant, the values indicate the change in the dependent variable (TSCC) linked to a one-unit change in the corresponding independent variable. The magnitude of the indicated values corresponds to the magnitude of the p-values, standard errors, and regression coefficients (β). The p-value establishes the statistical significance of the association between the independent variable and TSCC, while the regression coefficient (β) shows the strength of the relationship, the coefficient estimations’ accuracy is revealed by the standard errors (SE) offering a measure of the uncertainty, and the relationships’ statistical significance is shown by the p-values. The numbers above reflect how the dependent variable (TSCC) changes when the associated independent variable changes by one unit. Gender and TSCC have a statistically significant inverse connection. This shows that the degree of confidence and security in cryptocurrency crowdfunding is significantly influenced by gender. Education and TSCC have a statistically significant beneficial association. Higher educated people are more likely to feel secure and have higher levels of trust in crypto crowdfunding. Risk perceptions and TSCC have a statistically significant inverse connection. Lower levels of security and trust are linked to higher risk perceptions in cryptocurrency crowdfunding. Investor awareness and TSCC have a statistically significant inverse connection. Investors who are more knowledgeable are more likely to trust and feel more secure with crypto crowdfunding. Risks connected to cryptocurrency crowdfunding and TSCC have a statistically significant positive connection. Lower degrees of security and trust are correlated with higher perceived risks in cryptocurrency crowdfunding. Benefits and TSCC have a statistically significant positive connection. Higher degrees of security and trust are related to higher perceived advantages of investing in crypto-crowdfunds. Age and TSCC have no statistically significant association, meaning that the degree of TSCC is not influenced by investors’ age.

4.2.4. Investors Awareness in Crypto-Crowdfunds (IACC)

A multiple regression analysis is conducted to examine the predictors of IACC, where Gender, Age and Education are considered as control variables, β represents the coefficient of the parameter, SE represents standard error of the coefficient and p is the significant threshold chosen for this research.

I see that the model has a significant overall fit with f(2, 992)=24.16, p< 0.0001) and 14.8% of variance with IACC, meaning that approximately 14.8% of the variability in investors’ awareness can be explained by the predictors included in the model. Education shows a significant negative relationship with IACC (β=-0.0674, SE=0.018, p<0.05), indicating that individuals with higher educational levels tend to have lower levels of awareness in crypto crowdfunding. Risk Perception of Crypto Crowdfunding (RPCC) exhibited a significant negative impact with IACC (β=-0.119, SE=0.053, p<0.0001), suggesting that higher risk perceptions associated with crypto crowdfunding are associated with lower levels of awareness among investors. On the other hand, Benefits of Crypto Crowdfunding (BPCC) demonstrated a significant positive relationship with IACC (β=0.155, SE=0.034, p<0.0001), indicating that higher perceived benefits in investing in crypto-crowdfunds are associated with higher levels of awareness among investors.Gender showed a significant negative impact with both risk perceptions and investors’ awareness (β=-0.141, SE=0.048, p<0.0001), suggesting that being female is associated with lower levels of awareness in crypto crowdfunding. Age exhibited a significant negative impact with risk perceptions and investors’ awareness (β=-0.067, SE=0.084, p<0.005), indicating that older age groups are associated with lower levels of awareness among investors. However, there was no significant impact between Risks Associated with Crypto Crowdfunding (RACC) and investors’ awareness (β=-0.033, SE=0.04, p<0.05). The study conducted by Lin and Chen (2019) investigated the factors affecting investors’ intention to invest in initial coin offerings (ICOs), which is a form of crypto-crowdfunding, finding that risk perception, trust, and information asymmetry were significant factors that influenced investors’ intention to invest in ICOs. This is consistent with the current finding that risk perceptions have a significant negative relationship with investors’ awareness in crypto-crowdfunds. Lastly, I see that the plot of variance inflating factor for testing the multicollinearity of independent variable indicates that there is no collinear variable among the predictor (Tay, 2017). Examining the correlations between various factors and investors’ knowledge of crypto-crowdfunds (IACC) is the focus of the impact analysis in this sentence. The goal of the investigation is to determine which variables significantly predict investor awareness and how they connect to it. The values of the coefficients show how influential each predictor is on the dependent variable (IACC), which is represented by their relative significance. Greater influences on investors’ awareness are indicated by coefficients with larger values. The percentages denote the proportion of the variability in investors’ awareness (IACC) that can be accounted for by the model’s predictor variables. The model specifically accounts for 14.8% of the variation in investors’ knowledge. Moreover, investors’ awareness of cryptocurrency crowdfunds declines as education level rises. In contrast, a decline in education is linked to an increase in awareness. Investors’ awareness is correlated with a decrease in risk perceptions connected to crypto-crowdfunding. In contrast, a drop in risk perceptions is connected to a rise in awareness. Investor awareness is correlated with an increase in the advantages that investors experience from participating in cryptocurrency crowdfunds. On the other hand, a decline in awareness is connected to a decline in benefits perceived. Compared to male investors, female investors have lower awareness of crypto-crowdfunds. On the other hand, being a man is linked to greater awareness. Investor awareness is known to decline as people become older. On the other hand, younger age groups are linked to a rise in consciousness.

4.2.5. Risk Perceptions and Crypto-Crowdfunding (RPCC)

A multiple linear regression analysis was conducted to investigate the relationship between RPCC and various predictors in the context of crypto-crowdfunding, where Gender, Age and Education are considered as control variables, β represents the coefficient of the parameter, SE represents standard error of the coefficient and p is the significant threshold chosen for this research.

I see that Gender (β=-0.226, SE=0.028, p<0.00001) has a significant negative impact on risk perceptions. Male participants exhibited lower risk perceptions compared to female participants, indicating a significant gender difference in risk perceptions. The negative coefficient suggests that being male is associated with lower risk perceptions. Education (β=-0.076, SE=0.011, p<0.00001) has a significant negative impact on risk perceptions. Participants with higher education exhibited lower risk perceptions, indicating that higher educational levels are associated with lower risk perceptions. The negative coefficient suggests that higher education is linked to lower risk perceptions. Investors Awareness (IACC) has a significant negative impact on risk perceptions (β=-0.070, SE=0.019, p=0.0002). Participants who are more aware of investors in the context of crypto-crowdfunding exhibit lower risk perceptions. The negative coefficient suggests that greater investor awareness is associated with lower risk perceptions. Trust and Security (TSCC) has a significant negative impact on risk perceptions (β=-0.069, SE=0.025, p=0.0076). Higher levels of trust and security in crypto-crowdfunding are associated with lower risk perceptions. The negative coefficient suggests that higher trust and security are linked to lower risk perceptions. Perceived Benefits (BPCC) has a significant positive impact on risk perceptions (β=0.111, SE=0.020, p<0.00001). Participants who perceived more benefits associated with crypto-crowdfunding exhibit higher risk perceptions. The positive coefficient suggests that higher perceived benefits are associated with higher risk perceptions. Age does not have a significant impact on risk perceptions (β=0.014, SE=0.014, p=0.256), in fact participants’ age does not play a substantial role in influencing their risk perceptions in the context of crypto-crowdfunding, indicating that Age is not a significant predictor of risk perceptions. The finding that investors awareness and trust and security concerns are negatively associated with risk perceptions is consistent with previous research that has highlighted the importance of these factors in shaping risk perceptions related to cryptocurrencies and blockchain technology (Kshetri and Voas, 2018). On the other hand, the positive relationship between perceived benefits and risk perceptions suggests that investors who perceive more benefits associated with crypto-crowdfunding may also perceive higher risks. These findings suggest that risk perceptions in the context of crypto-crowdfunding are influenced by various factors and that interventions targeting these factors may help to reduce risk perceptions and increase adoption of crypto-crowd funding. Lastly, I see that the plot of variance inflating factor for testing the multicollinearity of independent variable indicates that there is no collinear variable among the predictor (Gwelo, 2019).

The impact study is a multiple linear regression done to look at how RPCC (risk perceptions in the context of crypto-crowdfunding) and other variables relate to one another. The investigation tries to comprehend how various factors affect risk perceptions in relation to cryptocurrency crowdsourcing. Gender, Age, Education, Investor Awareness (IACC), Trust and Security Concerns (TSCC), and Perceived Benefits of Investment (BPCC) are the factors taken into account. The coefficients (β) and standard errors (SE) deriving from the analysis of multiple linear regressions are referred to as the magnitude of the values. The standard errors give a sense of the degree of uncertainty around these estimates, while the coefficients show the estimated impact of each predictor on the RPCC. The results does not specifically identify the data (coefficients and standard errors) as units or percentages. The change in the dependent variable (RPCC) corresponding to a one-unit change in the predictor variable is represented by coefficients in the context of linear regression, however. Given that gender (representing male participants) and education are both strong negative predictors of risk perceptions (RPCC), an increase in either of these variables is related with a reduction in RPCC. In other words, individuals who are male and have a higher level of education typically see crypto-crowdfunding as having lesser risk. Since trust and security concerns and investor awareness of investors are both strong negative predictors of risk perceptions (RPCC), a rise in these values is connected with a drop in RPCC for IACC and TSCC (values). In other words, individuals who are more aware of these problems tend to see crypto-crowdfunding as having lesser risk. If the benefits of crypto-crowdfunding are more widely acknowledged, this will raise risk perceptions (RPCC), which is a substantial positive predictor of BPCC (value). In other words, participants in cryptocurrency crowdfunds tend to view risk as higher when they see more advantages. Age (value) does not significantly affect risk perceptions in the context of crypto-crowdfunding, according to the modest and statistically insignificant coefficient (p=0.256). As a result, changes in age do not significantly alter how people perceive risk.

Lastly, I provide various statistical tests for the analysis of the differences between the observed values and predicted values in a statistical model (residual analysis): the normality test using the Shapiro-Wilk test indicates that the data is not normally distributed; the Breusch-Pagan test results indicates that there is evidence of heteroskedasticity; the Durbin-Watson test of autocorrelation indicates that there is first-order autocorrelation in the residuals, furthermore the plot of multicollinearity using variance inflating factor indicate all the variables in the model are not collinear since none of the value is greater than 5 (Kock, 2012).

5. Discussion

The introduction of blockchain and cryptocurrency within crowdfunding platforms can play an imperative role in ensuring trust among the fund backers (Markande and Dagade, 2022).

It can enhance transparency, efficiency, reliability, and convenience, and restore trust among the crowd funders and will serve as a crucial starting point for other research of a similar nature that must be conducted in order to advance and address numerous problems and difficulties related to the field of crowdfunding, blockchain technology, and cryptocurrency (Naclerio and De Giovanni, 2022).

This research surveyed 1000 respondents to investigate their attitudes and perceptions towards cryptocurrency and crowdfunding. The majority of respondents were from Europe, followed by the USA and India. The gender distribution of respondents was slightly skewed towards females, and the average age of respondents was between 41-50 years old. The educational level of respondents varied widely, with the highest percentage of respondents having a diploma, followed by a bachelor’s degree. The study also examined the factors that influence funders’ decisions to invest in crypto-crowdfunding by comparing the correlation between Risk Perceptions and crypto-crowdfunding (RPCC), Investors Awareness and crypto-crowdfunding (IACC), Trust and Security and Crypto crowdfunding (TSCC), Benefits pertaining to investments in crypto-crowdfunds (BPCC), and Risks associated with crypto-crowdfunding (RACC) in relation to demographic variables such as Gender, Age, and Education. The results indicate that demographic variables are significantly associated with the variables studied. Specifically, Gender and Education are associated with RPCC, Age and Education are associated with IACC, Education is associated with TSCC, and Gender, Age, and Education are associated with BPCC and RACC. The study also found that RACC has a significant positive relationship with BPCC, while BPCC has a significant negative relationship with TSCC, RPCC, and a significant positive relationship with IACC. TSCC has a significant positive relationship with IACC and RPCC, while IACC has a positive significant relationship with RPCC (Wilson et al., 2019).

Thus, by this study, I find that the popularity of a project, innovation of the project, risk perceptions, investors’ awareness, inflation, decentralisation, and transaction cost all impact the investor’s decision to invest in crypto-crowdfunds. In order to enhance the attractiveness of crypto-crowdfunding and the funders’ decision of investing in crypto-crowdfunds, this research study suggests that the fundraisers must enhance the popularity of their project through the right marketing and also induce innovation. Businesses confront extraordinary hurdles as they recover from the coronavirus outbreak in the following years, necessitating a dedication to innovative approaches. Several firms have increased their assistance for innovation, but there is still room for advancement.

In light of the above, the findings of this study reveal that by enhancing the popularity and innovation pertaining to the projects, the fundraisers can attract funders to invest in the project. I also find that investors’ awareness and risk perceptions play an imperative role in impacting their decisions to invest in crypto crowdfunds. Thus, enhancing the transparency of the projects and targeting the correct audience for investment purposes can prove to be crucial for influencing funders’ decision of investing in crypto-crowdfunds. The results also reveal that with risk perception and benefits pertaining to investments in crypto-crowdfunding, gender becomes statistically important. Furthermore, the analysis shows that the age factor is important for understanding investors’ awareness and the risk associated with crypto-crowdfunding campaigns. While, with Risk Perceptions and crypto-crowdfunding (RPCC), Investors Awareness and crypto-crowdfunding (IACC), Trust and Security and crypto-crowdfunding (TSCC), Benefits pertaining to investments in crypto-crowdfunds (BPCC), and Risks associated with crypto-crowdfunding (RACC), education becomes statistically significant.

As a consequence, the implementation of blockchain technology and the general idea of cryptocurrency within crowdfunding platforms have the potential to play a significant part in assuring backers’ faith in platforms, having the potential to improve things like transparency, efficiency, reliability, and ease, as well as to restore faith among those who participate in crowdfunding. It may serve as a crucial starting point for other research of a similar nature that must be carried out in a bid to progress and identify multiple challenges and obstacles related to the field of crowdfunding, blockchain technology, and cryptocurrency. Thus, the results of this study may give people who want to raise money for their projects the opportunity to learn about the factors that investors consider when making crypto-crowdfunding investments (Jones et al., 2020). The primary objective of this research is to investigate the factors that influence the decisions of funders regarding whether or not to invest in crypto-crowdfunds. The research investigates the risks connected to crypto-crowdfunding. In addition, the practice of crowdsourcing via cryptocurrencies is still in its infancy (Kiong, 2022), thus the current study might be important for academicians, businesspeople, investors, startuppers, people who provide funding, people who raise money, and other stakeholders. Crowdfunding has become a popular means for small firms and enterprises to raise funds for expansion (Jamaluddin et al., 2022). Unlike traditional capital-raising methods, crowdfunding involves campaigns aimed at raising a specific amount of money for the business, thus enabling businesses - especially those with limited access to financial institutions - to achieve their objectives by successfully soliciting financial support from individuals (Kenworthy et al., 2023).

6. Conclusions

6.1. Final Considerations

This study provides empirical evidence on how socio-cultural, behavioural, and technological factors shape individuals’ engagement with crypto-crowdfunding, an emerging financing mechanism situated at the intersection of digital currencies, decentralised platforms, and fintech innovation. By analysing the perceptions of 1,000 respondents across diverse regions, the research demonstrates that investor attitudes toward crypto-crowdfunding are strongly influenced by demographic characteristics—such as gender, age, education, and country of origin—as well as by their levels of awareness, perceived risks, trust in digital platforms, and expected benefits. These findings highlight the critical role of socio-cultural heterogeneity in determining adoption patterns within digital-finance ecosystems.

The results of the current analysis indicate that blockchain-enabled transparency and decentralisation can enhance investor trust and expand access to funding opportunities, suggesting that crypto-crowdfunding may contribute to broader financial inclusion by lowering entry barriers and reducing reliance on traditional intermediaries. At the same time, the significant impact of risk perceptions and awareness underscores the necessity for robust risk-management practices, user-protection mechanisms, and clearer regulatory guidelines. Investors’ concerns about security, fraud, volatility, and platform reliability remain central challenges that must be addressed to ensure sustainable growth of crypto-based fundraising models. The study also shows that socio-cultural factors shape not only investors’ willingness to participate but also their interpretation of the benefits and dangers associated with digital currencies and decentralised platforms. These insights reinforce the importance of contextualising fintech adoption within broader behavioural, cultural, and regulatory environments rather than treating it solely as a technological shift.

This research contributes to the literature on fintech and digital finance by offering a nuanced understanding of how individual characteristics and perceptions influence participation in crypto-crowdfunding. As digital currencies and blockchain platforms continue to integrate into mainstream financial systems, policymakers, platform designers, and regulators must recognise the interplay between socio-cultural factors, financial inclusion objectives, and risk-management requirements. Strengthening regulatory clarity, enhancing transparency, and promoting digital-literacy initiatives will be essential for supporting the responsible development of crypto-crowdfunding and for maximizing its potential as an inclusive, trustworthy, and innovative financial tool.

6.2. Limitations and Future Research

Although this study provides valuable insights into the socio-cultural and behavioural determinants of investors’ participation in crypto-crowdfunding, in the current study several limitations should be acknowledged. First, the data were collected through an online questionnaire distributed across selected countries, which may limit the generalisability of the findings. Cultural, regulatory, and economic environments differ substantially across regions; therefore, future research should incorporate a wider set of countries—especially emerging markets where crypto-based financial inclusion is rapidly evolving. Secondly, the study relies on self-reported perceptions measured at a single point in time. Given the fast-paced development of digital currencies, regulatory policies, and fintech platforms, investor attitudes toward risk, trust, and security may change quickly. Longitudinal studies could provide a deeper understanding of how perceptions evolve alongside market volatility, regulatory interventions, and technological advancements. Thirdly, while the analysis captures key socio-demographic characteristics such as gender, age, education, and country of origin, additional socio-cultural variables—such as digital literacy, financial experience, risk tolerance, and cultural attitudes toward decentralisation—may offer further explanatory power. Future research could integrate these behavioural factors to build more comprehensive models of fintech adoption. Fourthly, the study does not explicitly examine the regulatory context of crypto-crowdfunding across jurisdictions. Regulation is a central factor in shaping platform credibility, investor protection, and perceived risk. Comparative studies exploring regulatory differences, compliance frameworks, and the effectiveness of risk-management mechanisms would significantly advance the field. Finally, while this research focuses on investor perceptions, future studies could investigate platform-level factors—including governance, smart-contract design, tokenomics, and fraud-prevention systems—to better understand how technological and structural features influence trust and participation. Thus, future research should adopt broader geographical samples, incorporate behavioural and cultural variables, analyse regulatory environments, and explore platform-level dynamics to deepen understanding of crypto-crowdfunding as a transformative component of digital finance.