Enablers for Growth of Cryptocurrencies: A Fuzzy–ISM Benchmarking

Abstract

Cryptocurrencies and their market capitalisation have experienced vibrant growth in the last few years. Their total market cap is more than USD 858 billion as of the date of writing and is growing, with nearly 21,984 tradeable cryptos in 530 exchanges. It is emerging as one of the biggest threats to the traditional fundraising market. The issue of the industry’s long-term viability and steady expansion is of paramount importance. Even though unsustainable and uneven growth could help boost economic activity in the short term, it would be detrimental in the long run because of the risk of extinction. This paper is one of the first attempts to identify the factors contributing to the growth of the cryptocurrency market and their effects. This paper is based on the hybrid MCDM methodology of research and uses fuzzy–ISM (interpretive structural modelling). This method is divided into three phases: identification, expert opinion, and interpretation. Sixteen factors were chosen from the previous literature and suggestions from industry professionals. Seven barriers have been framed based on the fuzzy–ISM analysis to better understand the impacts of and interrelationships among the identified barriers. The factors are further classified using fuzzy MICMAC into four major categories based on the drive power and dependence power extracted from the fuzzy matrix. This paper explains the importance of all identified factors as enablers of the acceptance of cryptocurrencies for investment and fundraising.

1. Introduction

A year ago, the legality of cryptocurrencies (CCs) had been questioned by many central banks across the globe. Now, the central banks of many countries, including India, are either planning to launch or have already launched their crypto and digital currencies to boost monetary transactions and the transfer of funds at the national and international levels. The cryptocurrency (CC) world is immune to old methods of transaction and distribution (Azman and Sharma 2020). No one will choose a traditional bank transaction over a cryptocurrency transaction after tasting cryptocurrency due to its flawless design, which offers peer-to-peer and transparent transaction options. (Zhong et al. 2020). If you want to raise money from people all over the world with few restrictions, cryptocurrency is the way to go. Crypto methods can also create a better environment for record keeping and the distribution and utilisation of collected funds. For example, fundraising to help COVID-19 occurred on many crowdfunding platforms; no regulatory body in the world could have tracked every dollar raised, and a sizeable percentage of these raised funds were misused. The pandemic scare disrupted economic and financial systems across the world. They were forced to reduce their dependence on traditional operations and partially shift to virtual forms of transactions.

Cryptocurrencies and their market capitalisation have experienced dynamic growth in the last few years. Today, their total market cap is over 858 billion U.S. dollars and is still rising; nearly 21,984 tradable cryptocurrencies are listed in 530 different exchanges (www.Coinmarketcap.com accessed on 12 December 2022). It is rapidly becoming one of the greatest obstacles of the established fundraising industry. Bitcoin, which was introduced in 2009 by a pseudonymous programmer and miner named Satoshi Nakamoto, is generally acknowledged as the first decentralised digital currency. However, the existence of virtual forms of currency can be traced to the late 1980s (Nakamoto 2008). Following the launch of other cryptocurrencies at the end of 2011, Litecoin had a modest amount of success and held the second-highest market capitalisation position until October 2014, when Ripple, a 2013 startup, overtook it. In order to make the Bitcoin protocol more suitable for daily transactions, Litecoin modified it by speeding up transactions (Schwartz et al. 2018).

CCs offer startup businesses an unparalleled capital-creation efficiency. The CC environment acts as a tool of liquidity enhancement, mainly in the early stages of business setup through crypto investors, to replace traditional methods of transferring funds and fundraising for business initiatives. In the CC process, digital coupons are sold as presale tokens that do not generally bestow ownership rights on the investors via a network of unregulated exchange platforms. Reward and risk factors in token investment vary from simple equity investment. Fundraising in cryptos is significantly less expensive than traditional fundraising strategies as a result of its lack of time-consuming regulatory requirements and restrictions coupled with the systematic acceptance of digital identity-based processes in place of paperwork or back-office record keeping in all phases of the process.

CC investment has been growing speedily in recent years after the success story of Bitcoin, established by pseudonymous Satoshi Nakamoto, through the use of blockchain machinery for cryptocurrencies in 2008. Many companies are using initial coin offers (ICOs) as a method for raising funds for their businesses; as a result of growth from the second quarter of 2015, ICOs outperformed VC financing for new business setups in the late third quarter of 2017. This data revealed that many startups that became unicorns had used cryptos to raise funds for their operations (State of European Tech Report 2022). Despite the fact that cryptocurrency does not have legal status in India, companies such as WandX, Drivezy, Nucleus Vision, BitIndia, Cashaa, and many others have used it to raise funds for their operations. Tokens now have a thriving secondary market, and cryptocurrency exchanges around the world serve as marketplaces where tickets can be bought and sold at wildly fluctuating prices. The credit card market is extremely complex and, now, too old for the general public to understand. Decentralised investment funds are analogous to crypto assets. The Commodity Futures Trading Commission was authorised to offer future contracts on Bitcoin at the Chicago Mercantile Exchange and the Chicago Board Options Exchange, the world’s two largest derivatives exchanges, beginning in early December 2017. However, the distribution of tokens to retail investors remains a mystery.

Despite the fact that CCs have piqued the interest of academics and professionals, very little structural research has been conducted in this area or on identifying the factors contributing to its growth. As one of the first efforts to investigate the growth factors of CCs, it is expected that this study will shed light on a number of important details regarding the potential underlying structure, or “detailed investment realities,” of CCs. The COVID-19 pandemic caused economic shock that the world is still recovering from. Any errors made by the CC market could also call into question its viability. One of the primary concerns of an economy experiencing such rapid growth is sustainability. For CCs to achieve economic sustainability, an increased financial inflow from both domestic and international sources would be required (Kumar et al. 2022).

It is critical to investigate the factors that contribute to the emergence of CCs and their significance. As a result, the purpose of this research is to identify the factors that influence the growth of CCs over time. This is one of the first studies to look into how these factors interact with one another. The primary goal of this study is to rank the dominance of the barriers. To establish a long-term market for cryptocurrencies, all factors must be examined for interdependence. Based on expert opinions, the research methodology employed follows an interpretive structural modelling (ISM) framework in a fuzzy environment.

This research is meant to provide the following useful insights:

• The discovery of causal factors and an elaboration on how they affect CCs;
• The production of a model that can be used by researchers, government officials, and business leaders by making use of the knowledge of a panel of experts;
• A consideration of the application of interpretive structural modelling (ISM) and MICMAC (matrices d’impacts croises multiplication appliqué a U.N. classement) in a fuzzy classification.

This research paper is structured as follows: an overview of cryptocurrency in Section 1, a discussion of the prior literature relevant to the study in Section 2, and a framework for the research in Section 3. Section 4 and Section 5 cover the structured questions and the application of ISM in a fuzzy calculation. The results are covered in Section 6. Section 7 discusses recommendations and applications, while Section 8 elaborates on constraints and the future range of research.

2. Literature Review

A complete understanding of cryptocurrency, initial coin offers, methods of fundraising, the research framework used, and fuzzy environments will be provided in this section to establish a plan for the study.

2.1. Literature on Cryptocurrency and Other Methods of Fundraising

NuShares/NuBits have emerged as the most traded currency in recent history based on a dual currency model unconnected from the previously used single currency idea (Lee 2014).

The authors of some of the literature have attempted to determine the relationship between cryptocurrency and banks and the transactions between them; in this sense, banks and other financial institutions have a significant competitive benefit over crowdfunding and other decentralized methods, including Bitcoin (Dimon 2016). All regular cash transactions at a bank can be programmed into a blockchain as an additional protocol cover (Wyatt 2014) with the technical support provided by startups, such as 21.com, that have developed software that converts any regular computer system into a Bitcoin-enabled computer system (Popper 2016). The valuation and risk calculation methods for cryptocurrency are very similar to those of DAO (decentralized asset organisation) tokens based on pure technical analysis, i.e., their historical prices and correlations with any physical asset, e.g., stocks, gold, fiat currencies, etc. (Drakakis 2008; du Rose 2015, 2016; Crunchbase 2016). The recent sudden flood of CCs in the market has caused confusion among potential investors about their optimal structure. The uncertainties of the CC market have spurned a search for optimal token sale mechanisms (Vitalik Buterin 2017; WINGS Magazine 2017).

From the literature survey, Kumar et al. (2022), Baur and Lucey (2018), Urquhart (2017a, 2017b), and many more have shed light on various significant barriers to the growth of the initial coin market and crypto assets.

The traditional method of raising capital is through an IPO (initial public offering) of financial assets, which benefits both the company seeking capital and the investors seeking a return on their investment (Ritter 1984). As a result of the “winner’s curse problem,” inexperienced retail investors who put all of their money into initial public offerings (IPOs) may end up with nothing (Rock 1986). Based on two signals and attributes, it has been observed that initial public offerings (IPOs) are typically underpriced, making them a promising investment opportunity (Grinblatt and Hwang 1989; Jegadeesh et al. 1993). Because of the rules and openness of book building, IPOs are a popular investment tool (Khurshed et al. 2014). Many works of literature have highlighted the various reasons for IPO underpricing in different market situations: Baron (1982), Muscarella and Vetsuypens (1989), Welch (1989, 1992), Lowry and Shu (2002), Boehmer and Fishe (2001), Krigman et al. (1999), Bubna and Prabhala (2011), and others.

2.2. Fuzzy–ISM and MICMAC

With the help of a comprehensive, methodical model, factors in interpretive structural modelling (ISM) are ranked according to their degree of direct or indirect connection. The use of ISM in various domains of study is not new. The research of Warfield (1974) and Sage (1977) can be used to trace the development of the framework. In studies on the supply chain, the ISM method has been employed to investigate the connections between various determinants of vendor choice (Mandal and Deshmukh 1994; Mohammed et al. 2008). This framework has been used in the domain of finance and accounting for factors related to retirement by Kumar et al. (2019). The ISM approach has been used to create a matrix of conservation-focused relationships between the elements and management of financial inclusion and the various financial needs for cryptos (Kumar et al. 2020, 2022). The hybrid framework for interdependence and feedback relationships in Chinese industries is based on the hybrid multidimension–scaling techniques ISM and ANP (Huang et al. 2005). The ISM methodology has been used to study green suppliers and vendors in the automobile sector (Kannan et al. 2008; Mathiyazhagan et al. 2013). In addition, fuzzy TOPSIS (the technique for order of preference by similarity to ideal solution) is added to the ISM framework as a combined strategy for analysing the standing criteria in the domain of reverse logistics providers for third parties (Kannan et al. 2009). Healthcare issues in rural areas can also be resolved with the help of the ISM (Kumar and Sharma 2018). Raj et al. (2008) employs a structured ISM approach to identify enablers of the flexi manufacturing process and examine how they may collaborate. Knowledge-management-related studies using the ISM framework for ranking factors (Singh et al. 2003; Singh and Kant 2010; Tabrizi et al. 2010) literature surveys, (Lai et al. 2008; Shahabadkar et al. 2012; Zhu et al. 2012), and several others have provided overviews of the ISM-based and hybrid ISM-based literature published across a variety of sectors.

2.3. Research Gap

Literature based on ISM and hybrid ISM is widely available in many sectors, including several financial market applications and systems. As the cryptocurrency industry grows, authorities are becoming more concerned about its sustainability and transparency. For the protection and benefit of investors, authorities must understand the development factors of these new cryptos and virtual assets. Their accelerators have only been briefly discussed in the literature. Our search yielded no organised literature ranking these characteristics using ISM or any other model. This study aims to fill this knowledge gap by investigating the interplay between the factors influencing the growth of the CC market.

Table 1. Description of CC factors.

Sr. No.	Factor	Description	Supported Literature
1	No or minimal time commitment	In many instances, CC can complete the collection process in under an hour.	On opinion of expert
2	Lack of or minimal regulation	In most countries, CC does not adhere to security or income tax laws.	Grace Leong (2017), Dimon (2016)
3	Low price	Issue costs are significantly lower than with traditional fundraising techniques.	Den Murphy (2017)
4	No location limitation	There are no geographical limitations to working on a digital platform. CC can raise money from any source.	Sanchez (2017)
5	The utilisation of unaccounted Funds	Some platforms do permit the channelling of unaccounted funds into cryptocurrency.	On opinion of expert
6	Lack of funds for new businesses	Raising money through equity sales is not possible for startups.	Sanchez (2017), Dimon (2016)
7	Not being able to raise money through IPOs or another traditional method	Even after their extensive business histories, many companies and firms are unsuited for raising money through IPOs.	Sanchez (2017), Dimon (2016), Kumar et al. (2022)
8	Competition being fierce in the traditional financial market	Everyone is familiar with traditional markets, so there is a heavy rush to raise money there.	Sanchez (2017), Dimon (2016)
9	Heavy reliance on banks and the need for another investment banker to raise public funds	The traditional form of fundraising needs investment bankers and networks of financial institutions.	Sanchez (2017), Sehra et al. (2017)
10	Increased IT and digital sources	Digital sources serve as a vehicle for the proliferation of crypto assets.	Sanchez (2017), Dimon (2016)
11	Supportive environment	The current external environment is favourable enough to absorb such creative assets for fundraising.	Sanchez (2017), Dimon (2016)
12	Exceptional financial literacy	Financial literacy has recently increased dramatically, especially among urban residents.	Sanchez (2017), Dimon (2016), Sehra et al. (2017)
13	The innovation in crypto assets (equity shares back some cryptos)	Now, crypto coins are backed by specific physical and financial assets, so investors have more faith in them.	Sanchez (2017), Dimon (2016)
14	The desire for higher returns among investors	Investors are constantly seeking higher returns in less time.	On opinion of expert
15	Comparatively small investment	One U.S. dollar is all that is needed to invest in the CC platform.	Sanchez (2017), Dimon (2016), Sehra et al. (2017), Presscoin.com
16	Not enough stock exchanges or suitable infrastructure for raising money	Many nations lack the fundraising infrastructure necessary to raise money, leaving cryptos as a viable option.	On opinion of expert

shows the sixteen factors used in this study, twelve of which were gathered from the published literature, and the remaining four were suggested by industry and academic experts. Using this decision-making approach, these detected elements were prioritised.

3. Research Methodology

This section provides a detailed explanation of methodology employed in this study to address the issues raised in the previous section. Figure 1, below, explains the stepwise framework of the method followed in this study.

The rising demand for CCs can be attributed to new firms increasing their capital requirements. Current businesses have flocked to the CC race in order to attract new investors and expand their operations. Despite the absence of regulatory support in many countries, the sector has grown by more than 80% in the last four years. One of the most difficult aspects of this research is determining which factors are most important to CC growth and ranking them accordingly.

The observed accelerating factors were rated in a fuzzy environment using ISM. The steps described below are part of the ISM strategy, as explained by Kumar et al. (2019, 2020, 2022):

Step 1:

Make a note of factors that will be considered during the study;

Step 2:

Establishing a contextual relationship between all variables selected in step 1 for a paired investigation;

Step 3:

Create a structural self-interaction matrix (SSIM) for the relationships between variables when paired up;

Step 4:

Develop and assess a transitivity reachability matrix. ISM theory relies heavily on the transitivity relationship between variables. A is connected to C if element A is connected to element B, and element B is likewise connected to element C;

Step 5:

Subdivide the reachability matrix into several levels (initial relationship matrix) to make an IRM.;

Step 6:

Eliminate transitive links from a directed graph according to the relationships revealed by the matrix’s reachability entries (final relationship matrix) in the process of making a functional reachability matrix (FRM);

Step 7:

Examine the conceptual incompatibility of the established ISM model from the previous phase. It is possible some necessary adjustments will be made as needed;

Step 8:

Use a MICMAC and fuzzy MICMAC analysis to group all variables into four categories to ensure a steady relationship.

Figure 2, below, is a flowchart depicting the aforementioned procedures.

Both micro- and macro-level variables could be the root of CC growth. CCs have no geographical restrictions, low costs of issuance, and require a high level of financial literacy, among other important variables. As stated in Table 1, sixteen criteria were derived from published research and expert opinion.

4. Formation of the Questionnaire

Due to the unique mechanics of cryptocurrencies, the growth trajectory of CCs differs significantly from that of standard capital market assets. ISM, a method for enhancing conversion in convoluted circumstances, contributes to this study’s primary objective. Twelve criteria from prior research are utilised to explore the growth drivers of the CC market, with four more criteria added as a result of expert discussions. These financial market specialists are senior academics and senior professionals from various universities. Thirty-five specialists were initially selected and contacted, but, after continual conversation, only twenty-one agreed to participate in the project. All expert responses were collated in two stages and sent to two experts. These specialists were selected based on their unique qualities as researchers with at least ten years of academic experience and seven years of industry experience. After gathering all responses and holding brainstorming sessions, a single consolidated response matrix was created.

5. Application of ISM

The creation of SSIM (structural self-interaction matrix) marks the beginning of the ISM technique. This matrix was created as a deliberate representation of the contextual connections between chosen components using professional judgement.

Each component’s directional relationship with two others is represented by a set of four symbols. For example, in a hypothetical relationship between a component and the factors i and j, the symbol “V” would indicate that factor i aids factor j, “A” would indicate that factor j aids factor i, “X” would indicate that factor (i or j are represented by the horizontal) j and i aid each other, and “O” would indicate that there is no relationship between i and j. The SSIM for the listed CC growth factors is offered in

Table 2. SSIM for CC.

Factors	I	II	III	IV	V	VI	VII	VIII	IX	X	XI	XII	XIII	XIV	XV	XVI
I	X	O	O	A	O	A	O	O	A	A	X	O	V	V	O	O
II		X	V	O	X	V	V	V	V	O	X	O	V	V	V	V
III			X	O	X	O	O	V	X	A	O	O	A	V	X	A
IV				X	V	V	V	V	X	A	A	O	A	O	O	O
V					X	V	O	O	O	A	A	O	A	V	O	A
VI						X	X	O	V	O	A	O	V	O	O	X
VII							X	X	V	O	V	A	A	O	O	X
VIII								X	V	X	O	O	X	O	O	X
IX									X	O	X	O	O	O	A	X
X										X	X	O	X	O	O	O
XI											X	O	X	O	A	V
XII												X	V	O	O	X
XIII													X	X	V	O
XIV														X	A	O
XV															X	O
XVI																X

.

Through applying the rules below to the SSIM data, we can generate a reachability matrix by converting the value in each cell to 1 or 0 in the matrix:

• The V of a given SSIM cell and the reachability matrix value for that cell change to 1 for the (i, j) position and 0 for the (j, i) position.
• If the SSIM value is A, the reachability matrix entries (i, j) and (j, i) will switch roles to 0 and 1, respectively.
• Reachability matrix entries (i, j) and (j, i) are modified to read “1” if X is the value.
• If the value is 0, then both the (i, j) and (j, i) entries in the reachability matrix will be set to 0.

Table 3. Reachability matrix after transitivity.

Factors	XVI	XV	XIV	XIII	XII	XI	X	IX	VIII	VII	VI	V	IV	III	II	I
I	1	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1
II	0	1	0	1	1	0	0	1	0	1	0	0	0	1	0	0
III	0	0	1	1	1	0	0	0	0	0	0	0	0	0	1	0
IV	0	0	0	1	1	0	0	0	0	0	0	0	1	1	0	0
V	0	1	0	1	1	1	0	1	0	1	1	0	0	1	0	0
VI	0	0	1	0	1	1	1	1	0	1	0	0	0	1	1	0
VII	1	0	1	1	0	1	1	0	1	0	0	0	0	0	0	0
VIII	0	0	0	1	1	0	0	1	0	0	1	0	0	1	0	0
IX	1	0	1	0	1	0	0	1	1	0	1	0	1	0	1	1
X	0	0	0	1	1	0	0	1	0	1	1	1	1	1	0	0
XI	1	0	1	1	1	0	0	1	0	1	1	0	0	1	0	0
XII	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	0
XIII	1	0	0	1	1	0	0	1	0	1	1	0	1	0	0	0
XIV	0	0	0	1	1	1	0	1	0	1	1	1	1	1	0	0
XV	1	1	1	1	1	1	0	1	1	1	1	1	0	1	1	0
XVI	1	0	1	0	0	0	0	0	0	0	0	0	1	1	0	1

shows the final reachability matrix, which is used to establish the reachability and antecedent set for each factor (Warfield 1974). The set of elements that can help reach this goal, or the rows with values of 1, is called an element’s reachability set. A given element’s antecedent set includes not only the given element but also all factors with a value of 1 in the corresponding column that contribute to its realisation. The ISM’s top-level hierarchy displays factors with equivalent intersection values, which were identified by computing the intersections of the reachability and antecedent sets. Next, the character sets with levelling factors removed were examined.

Table 4. A fuzzy scale of 7 points was used in the calculation.

Outcome	Full	Very High	High	Medium	Low	Negligible	No
Triangular Value	(1,1,1)	(0.7,0.9,1)	(0.5,0.7,0.9)	(0.3,0.5,0.7)	(0,0.3,0.5)	(0,0.1,0.3)	(0,0,0)

shows that the level identification processes for all sixteen elements took a total of five rounds to complete. The estimated hierarchy levels of these chosen variables were used to generate a digraph, which acts as the highest level of ISM. Iterations were used to build the structural digraph, which is depicted in Figure 3. Directional arrows from factor i to factor j represent the connections between these variables.

MICMAC (matrices d’impacts croises multiplication appliqué a un classement, i.e., cross-impact matrix multiplication applied to classification) is calculated according to the principle of matrix multiplication (Sharma et al. 1995).

Researchers have been increasing the responsiveness of MICMAC analyses by employing the fuzzy set theory in order to bypass the constraints of the ISM model. To add a new dimension to the analysis of dependencies among barriers, we employed a method called fuzzy MICMAC (Arya and Abbasi 2001). To transform BDRM data into FDRM, it is important to collect information from the same authorised expert in order to develop a fuzzy MICMAC (FMICMAC; fuzzy direct relationship matrix). The final BDRM was used to create the FDRM, which is described as a function of membership with a score of [0, 1] for the fuzzy sets used. A fuzzy scale based on a seven-point scale has been used for this fuzzy evaluation, as shown in Table 4.

The same experts have been contacted to gather their responses, and they provided ratings for obtaining a direct reachability matrix. A triplet set represents the triangular fuzzy number ‘U.’ This set is formed by specifying a minimum (a), a median (b), and a maximum (z) value in the range (a < b < z), from which we can further refine the fuzzy triangle function. These values are the coordinates of the triangle’s three vertices, represented by the fuzzy set U. The results computed using the fuzzy scale must be defuzzified before they can be used to calculate the FDRM. The parameterised Best Non-fuzzy Performance (BNP) of Equation (1) is as follows:

$$BN{P}_{ij}=\left[\frac{\left(z-a\right)\times \left(z-b\right)}{3}\right]+z$$

(1)

Repeated multiplication (C = max k {min (i, j)}) of the fuzzy matrix is used to calculate the FDRM’s strength until convergence is achieved. This convergence point is reached when the driving and dependent forces of the specified factors have stabilised or are cyclically varied with the given periodicity. The primary goal of FMICMAC is to produce a graph that shows the relationship between the barriers’ dependence, on the X-axis, and its strength, on the Y-axis. These relationships have been sorted into four groups based on the importance of the requirements; the various categories are as follows:

• In the first quadrant, autonomous types operate relatively independently of the structure, with weak driving and dependence forces.
• Types that fall into Quadrant II are highly dependent but lack a significant motivational force.
• Types fall into Quadrant III, linkage types, because their dependence and driving power are inadequate. All other parts of the system revolve around them, but they are the most crucial. These variables are particularly sensitive to even small shifts in other variables.
• According to the independent category, Quadrant IV possesses high driving power but low dependence power.

MICMAC and FMICMAC analyses were performed to categorise the study’s factors. To determine driving and dependence powers, analyses were conducted using professional judgement. See Figure 3 for a visual representation of the identified factors within the full five-stage integrated model developed using ISM. The factors “innovation in crypto assets” and “desire for higher returns among investors,” at 13 and 14, respectively, are at the apex of the chain of dependencies shown in

Table 5. Driving power (DRP) and dependence power (DEP) on stabilised FDRM.

Factors	DEP	DRP	Factors	DEP	DRP
1	14.2	10.3	9	13.3	11.4
2	2.5	13.9	10	8.6	12.7
3	12.5	8.5	11	9.1	12
4	12.2	11.6	12	8.5	12.1
5	11.3	10.3	13	11	11
6	13.4	10	14	11.7	12.3
7	13.4	11.5	15	9	8.5
8	13.3	11	16	13.3	11.3

. Only two of the four quadrants in the FMICMAC graph contain these identified factors. Section 6 provides a further explanation of these findings.

6. Results

The growth of CCs is undoubtedly very high, yet its lack of popularity among common investors is a significant problem for its sustainability. Overcoming this barrier is essential for CCs to be considered one of the main alternatives to traditional fundraising methods. Its multifold market growth in the last few years has led to it having sturdy relationships with other parts of the economy, mainly due to its popularity among startups. Such associations pressure regulators to create a global legal structure for the long-term sustainability of CCs. Moreover, developing such a worldwide legal framework is no easy feat, and there are many obstacles to overcome before CCs can be incorporated into regular business practices. No one can guarantee a stable structure without first collecting data on these issues. This research is one of the first to try using a well-tested ranking model to recognise CC factors in a fuzzy setting. The foundation of this effort is the principle of ISM modelling within a fuzzy environment, with data drawn from the aforementioned literature and expert consultations. The relationships between these factors is thus represented using a framework derived from the ISM model. In the expert consultations, once the goal was been explained to the expert, the inputs were collected in two phases: first, on a binary scale and then, in the second phase, on a fuzzy scale. Finally, MICMAC produced a graph that showed the relationships between driving power, on the Y axis, and dependence power, on the X axis. The full meaning and interdependencies of each factor can be understood with the help of a fuzzy MICMAC analysis shown in Figure 4.

The linking category, represented by Quadrant III, includes all components except for those that exhibit a lack of or weak regulation. Both the dependent and driving powers of these elements are extremely high. The impact of such variables on the system is substantial. Any change made to them will ripple out and affect other parts of the system. The vast majority of causes for CCs’ meteoric rise in popularity may be found here. All these things help make CC a viable alternative to traditional fundraising strategies. The sole factor that falls under Quadrant IV is its absence of or limited regulation, so it largely moves independently of other system components and is not affected by them. It is not something that can be managed by the industry participants individually.

Although the factor “lack of or minimal regulation “is one of the most important dimensions affecting the growth of this innovative fundraising method and its acceptance by ordinary investors, it cannot be controlled directly by the associated members of the CC industry. However, regulations may change after central banks start issuing their crypto and digital currencies to the market. Some factors, such as “exceptional financial literacy,” may act as supporting elements in some parts of the globe while working as negative factors in a significant part of the growing economy.

This research is one of the first attempts of its kind; very little literature is available to compare the results. We found that our research output has some resemblance with Łęt et al. (2023) in finding drivers of popularity for stable crypto coins, with Campino et al. (2022) in identifying determinants of initial coin offer success, and with Nurbarani and Soepriyanto (2022) in discussing the variables that people consider when making the decision to invest in cryptocurrency.

7. Recommendations and Implications

The financial system of a country plays a vital role in not only the development of its economy, but also in its sustainability. CCs are becoming a preferred method of capital creation as a result of increased digitalisation. The operation, regulation, and understanding of CCs is becoming very important for investors from developing economies. Investment needs can be achieved by understanding the leading factors that contribute to the acceptance of CCs. When making the choice to invest in CCs, decision-makers should focus less on the independent factors themselves. As no identified factors fall under the autonomous category, all factors should be considered at regular time intervals when deciding to use a CC to achieve desired financial goals. All elements except “lack of or minimal regulation” fall under the linkage category of the quadrant, which is crucial in decision-making as their value may affect the other factors. The results of this study undoubtedly provide insights to regulators. The current study can also help central banks plan for issuing CBDC and can increase its market acceptability. Market regulators should take some measures to create a safe and supportive environment for CCs, thus enhancing the entire financial industry.

8. Limitations and Scope of Future Research

In this study, the developed framework based on a hybrid ISM and MICMAC model was tested in a fuzzy environment with sixteen factors contributing to the growth of the CC market. Some factors have been selected but not used in the framework as a result of their unsuitability for the purposes of this research. In the future, this study can be used to form a model for the valuation of CCs, based on the importance of the selected factors, to be used by investors for making investment decisions regarding CCs.