Crowdfunding has evolved from traditional forms to more diverse and specialized platforms. Kim & Hann (2013) note that crowdfunding platforms facilitate fundraising by enabling individuals to solicit financial contributions from a large and dispersed group of online users, democratizing access to capital. This democratization argument positions crowdfunding as an inclusive financial tool that empowers individuals, particularly those with socially beneficial initiatives ( Pak & Wash, 2017 ).

Alegre & Moleskis (2016) identify five main types of crowdfunding: donation-based; reward based; crowdlending; crowdequity and mixed models. Haas et al. (2019) introduce a novel categorization based on motivations: philanthropic, hedonistic, and profit-oriented crowdfunding, highlighting the importance of understanding backer motivations.

This categorization aligns with Alhammad et al. (2021) and Camilleri & Bresciani (2022) , who refine the categories to include donation, reward, loan (or debt) and shares, reflecting the market’s growing sophistication and ability to meet diversified financing needs.

However, individuals with financial resources and fundraising expertise are more likely to succeed, aligning with the “rich get richer” argument ( Swart, 2014 ). This phenomenon, known as the Matthew Effect ( Perc, 2014 ), describes the tendency for wealth and power to accumulate disproportionately among those already advantaged. In network science, this principle aligns with preferential attachment, where highly connected nodes attract more connections over time, reinforcing their centrality ( Li et al., 2025 ).

Crowdfunding is hailed as a democratizing force in finance, but it doesn’t escape this, Matthew Effect. While it enables entrepreneurs to access capital without traditional intermediaries ( Mollick & Robb, 2016 ), its democratizing influence is limited by promotional challenges ( Galuszka & Brzozowska, 2017 ). The rich get richer phenomenon persists, with successful crowdfunders learning more while those who fail often give up ( Pak & Wash, 2017 ).

Crowdfunding is increasing in areas with falling housing prices, particularly in low socio-economic status areas, but these projects are often unsuccessful ( Kim & Hann, 2018 ). Efforts to level the playing field can paradoxically result in a less equitable distribution of funds ( Geva et al., 2024 ). While crowdfunding expands access to financing for small businesses generally not served by institutional investors, it faces serious adverse selection issues ( Dolatabadi et al., 2021 ).

Unlike previous fragmentary analyses, this paper integrates multiple dimensions of inequality, offering a holistic perspective on the systemic challenges of crowdfunding. It provides a comprehensive perspective on how crowdfunding, as alternative financing mechanism, is analysed and what future research directions could deepen our understanding of its structural inequalities.

Using PRISMA, this paper sheds light on the extent to which crowdfunding reinforces financial disparities. The research question addressed is: How do structural inequalities related to geography, race, gender, and institutional hierarchies shape crowdfunding outcomes, and to what extent do regulatory and technological frameworks mitigate or exacerbate these disparities?

With the crowdfunding market’s growing sophistication, our main research objective is to examine disparities in crowdfunding success based on geography, race, gender and institutional hierarchies; assess the impact of regulatory frameworks on campaign performance and evaluate the role of technology in reducing funding disparities.

The rapid technological evolution of crowdfunding platforms, including the potential of blockchain technology to improve transparency and security, reduce transaction fees, and increase trust, is noted by Prathyusha et al. (2024) . Katsamakas & Sun (2020) investigate using machine learning algorithms to predict crowdfunding success, potentially improving decision-making for both platforms and investors.

This study explores how advanced technologies integrated into crowdfunding platforms influence governance and reinforce structural inequalities. It begins by tracing the evolution of crowdfunding models and highlighting disparities related to geography, race, and institutions. Using the PRISMA method, the literature review systematically identifies studies on funding inequalities. The findings reveal key trends, including the Matthew effect, and discuss financial inclusion, platform governance, and policy implications. The paper concludes with a summary of insights, limitations, and suggestions for future research.

This paper follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology ( Moher et al., 2009 ) to ensure transparency and reproducibility in the review process. This approach is widely used in finance, economics, and social science research. We describe the search strategy to identify relevant studies on crowdfunding disparities, define the eligibility criteria for inclusion, outline the methods for data extraction and analysis, and provide an overview of the literature reviewed.

According to the PRISMA framework, the article selection process includes identification, screening, eligibility and inclusion ( Deepika et al., 2023 ). We conducted a systematic search of peer-reviewed English-language articles indexed in Scopus, Google Scholar and Web of Science up to January 31, 2025, in the fields of Social Sciences, Economics Business, and Finance. These databases were selected due to their comprehensive coverage of interdisciplinary research and their established use in systematic reviews.

The search strategy employed a combination of Boolean operators (AND, OR) and targeted keyword clusters directly related to the research topic. Boolean logic enabled us to build comprehensive search strings that captured the complexity of crowdfunding disparities.

Sample search queries included:

To structure the search process, we categorized our keywords into three thematic groups: 1. Crowdfunding mechanisms (equity crowdfunding, reward-based crowdfunding, donation-based crowdfunding, peer-to-peer lending).

2. Disparities and structural inequalities (inequality, disparity, bias, discrimination).

3. Key players and influences (investor behavior, donor motivation, platform regulation, algorithmic bias, financial inclusion).

These combinations were systematically applied across the selected databases (Scopus, Web of Science, and Google Scholar) to ensure both breadth and specificity in the retrieval of relevant studies. The structured approach aimed to maximize coverage while maintaining a focus on empirical and theoretical contributions related to inequalities in crowdfunding ecosystem.

To ensure the relevance and quality of the studies included in this systematic review, we established clear inclusion and exclusion criteria. Studies were included if they met the following conditions: published in peer-reviewed academic journals; explicitly focused on disparities or inequalities in crowdfunding outcomes; presented either empirical findings or a substantial theoretical discussion; and fell within the disciplinary boundaries of the social sciences, finance, or economics.

Conversely, studies were excluded based on the following criteria: the article was not written in English; constituted non-peer-reviewed literature, such as conference abstracts, unpublished theses; and it did not address structural inequalities or systemic disparities in the crowdfunding ecosystem.

A total of 114 papers were identified through systematic database searches, before the screening phase,50 records were excluded for the following reasons: duplicate records (n = 12),automatically ineligible records identified by screening tools (n = 28), other reasons, such as incomplete metadata or inaccessible full texts (n = 10), and 64 were registered, as shown in Figure 1 , encompassing a wide range of research on crowdfunding mechanisms, disparities in fund distribution, and the Matthew Effect in alternative finance.

Following identification, the 64 papers were reviewed based on their titles and abstracts to determine relevance. During this phase, 20 papers were excluded for not explicitly addressing crowdfunding inequalities or the Matthew Effect. Studies focusing primarily on crowdfunding as a marketing tool or innovation promotion mechanism were deemed irrelevant.

The remaining 44 papers were subjected to a full-text assessment to determine whether they could be included. This rigorous review excluded 11 more papers, as they did not sufficiently address crowdfunding inequalities or the Matthew effect. For example, some studies focused on platform algorithms and donor behaviour but had no direct link to wider financial disparities, did not meet the inclusion criteria.

As shown in Table 1 , 33 studies were selected explicitly studying crowdfunding inequalities or the Matthew effect, aligning with the central research themes of financial inclusion, resource distribution, and structural disparities in alternative finance.

Finally, the classification was based on recurring themes and keywords extracted during the data coding process, with each study assigned to one or more categories depending on its primary analytical focus. The criteria for categorization were guided by the type of inequality addressed (structural, racial, geographic, etc.), the scope of the study, and the population or mechanism under examination. This process followed an inductive approach informed by existing literature.

To ensure systematic and reproducible categorization, we developed keyword dictionaries for each thematic group. During coding, studies were assigned to categories based on the presence of these keywords in their abstracts, titles, or main texts. This keyword-based approach allowed for the identification of overlapping themes and facilitated the multi-classification of studies that addressed intersecting forms of inequality, as summarized in Table 1 .

Source: Authors.

This structured, keyword-driven approach allowed for consistent and transparent assignment of studies to thematic groups, reflecting the interdisciplinary and intersecting nature of inequality research in crowdfunding.

This section presents and analyses the results, focusing on descriptive analysis and literature classification. The descriptive analysis summarizes the dataset, while the literature classification organizes studies into thematic categories, providing a foundation for understanding the research landscape.

Table 2 provides an overview of 33 academic studies that examine various dimensions of inequality in crowdfunding. Each study identifies specific mechanisms through which inequality manifests, focusing on different aspects such as institutional hierarchies, socio-economic disparities, racial and gender biases, and geographic imbalances, as demonstrated in Table 3 based on thematic groups and key categorization keywords.

As shown in Figure 2 , the sum of studies across the various inequality categories (47) exceeds the total number of unique studies included in the analysis (n = 33). This apparent discrepancy arises because numerous studies address multiple forms of inequality and are therefore classified into more than one thematic category.

The classification system employed in this review permits each study to be assigned to multiple thematic groups if it addresses more than one type of inequality. For example, a paper examining racial disparities and health inequities in crowdfunding would be counted once in each relevant category.

This multi-classification approach more accurately captures the interdisciplinary and intersecting nature of inequality research, wherein individual studies frequently explore multiple, overlapping dimensions of disparity. For instance, a study investigating medical crowdfunding among minority populations may be classified under Health inequities and Racial and Gender disparities.

As shown in Figure 3 presents the distribution of studies on crowdfunding inequalities, categorized by year and type of inequality.

Each row in the graph represents a year, and columns categorize studies based on specific types of inequalities. The number in each cell indicates the count of studies addressing a particular form of inequality in that year.

Reputation-Based inequalities: Investigate how an individual’s previous reputation or social capital affects crowdfunding success, examining whether well-known entrepreneurs receive more funding than newcomers. These studies appeared sporadically, mainly in 2014, 2017, 2018, 2019, and 2021.

Several important patterns emerge from this dataset:

Figure 4 covers a broad spectrum, from individual user experiences (inequalities, inclusion barriers) to systemic influences (government policies, platform strategies), providing a global perspective while distinguishing between developed and developing economies.

As shown in Figure 4 , the majority of studies on crowdfunding and inequality are concentrated in the United States, with 20 out of 33 studies originating from this context. This predominance reflects the high academic and societal interest in crowdfunding outcomes related to racial and gender disparities, geographic inequalities, and health inequities within the U.S., where systemic racism, healthcare access gaps, and digital divides are persistent structural issues.

In Europe, research more frequently addresses civic crowdfunding, government involvement, and urban inequalities. European studies often examine the relationship between public funding and collective action, with a focus on how crowdfunding supplements municipal resources and exposes spatial disparities within cities.

Studies categorized as Global Contexts extend the analysis across national boundaries, emphasizing digital inclusion and the role of mobile money. These works highlight the diversity of crowdfunding adoption and outcomes under varying regulatory and infrastructural conditions

Canadian research, though limited in number, centers on healthcare and education crowdfunding, likely reflecting the influence of the country’s universal health system and the need to address funding gaps in education.

In Developing Economies, studies primarily investigate barriers such as inadequate digital infrastructure and limited financial inclusion. These structural challenges such as poor internet access and underdeveloped payment systems significantly shape the dynamics and effectiveness of crowdfunding in these settings.

As shown in Figure 5 , the types of documents reviewed also influence the thematic orientation of the research. Academic research dominates the literature (25 studies) and tends to engage deeply with systemic biases, structural inequalities, and theoretical frameworks. These studies often use qualitative and quantitative methodologies to analyze disparities, develop typologies, or explore underlying sociotechnical mechanisms driving unequal outcomes in crowdfunding.

Industry reports, though fewer in number, offer valuable insights into platform-level trends and disparities. These documents are often data-rich and practical, shedding light on real-world patterns such as success rates, platform algorithms, user demographics, and campaign types. Their emphasis is usually less on causality and more on trend analysis and market behavior.

Policy and government studies focus on the regulatory environment and how public policy influences crowdfunding ecosystems. These include white papers or governmental reviews that assess the role of legal frameworks, incentives, and oversight mechanisms. Such documents are crucial for understanding how structural change or regulatory intervention might mitigate or exacerbate inequalities in access to crowdfunding opportunities.

Figure 6 illustrates the thematic distribution of studies examining various forms of inequality within the crowdfunding literature. The most prominent category, structural inequalities, accounts for 38.3% of the reviewed studies. This is followed by research focused on institutional and reputation-based inequalities (21.3%), racial and gender disparities (14.9%), health inequities (12.8%), and geographic inequalities (8.5%). A small proportion of studies (4.3%) did not align clearly with these categories and were thus classified as uncategorized.

These categories were developed through a thematic synthesis of recurring topics, reflecting the most salient disparities identified in the literature. Each thematic group is described below:

Together, these studies highlight consistent mechanisms through which inequality is reinforced within crowdfunding ecosystems:

These findings call for critical attention to platform design, algorithmic transparency, and equity-oriented interventions to address the inherent disparities within the crowdfunding landscape.

Source: Authors.

By analysing these systemic inequalities, Table 1 establishes a foundational understanding of the research landscape and informs further discussions about how crowdfunding platforms may unintentionally reproduce and amplify broader societal disparities.

Notes: The table displays each study, the number of inequality categories it covers, and the specific categories assigned. Uncategorized indicates studies not assigned to a specific thematic group.

Ultimately, a truly democratized crowdfunding landscape requires systemic reforms that actively address inequalities rather than passively sustaining them. Without intentional corrective measures, crowdfunding risks becoming another financial mechanism that perpetuates, rather than mitigates, structural disparities in economic opportunity.