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Abstract

This study has conducted a meta-literature review examining the past, present and possible future trends of Fintech research using 360 selected articles published between 2006 and June 2020. Both quantitative and qualitative techniques were applied. In the quantitative approach, a bibliometric citation analysis using HistCite and VOSviewer software was conducted and the qualitative analysis covered the identification of four main research streams related to (i) Bitcoin and digital currency, (ii) crowdfunding, (iii) mobile payment, and (iv) blockchain. The study results highlight the most influential aspects of the FinTech literature, such as the leading countries, institutions, journals, authors, and articles. Suggestions for the potential future direction of FinTech literature have also been made.

Keywords

meta-literature review bibliometric citation analysis Fintech content analysis

Introduction

Financial technology (Fintech) is probably one of the most noticeable recent innovations in the financial sector. Its potential to radically transform the financial sector by providing convenience and security to financial transactions has been highly appreciated. Innovative entrepreneurs have primarily led FinTech so far. However, it has also gained the attention of academicians and is being critically followed by regulators. It is a broad term and applies to many innovations such as InsurTech, RegTech, Robo advisers and AdTech. According to Gai et al. (2018), FinTech is a novel technology used in the financial services industry. Zavolokina et al. (2016) have opined that FinTech is the marriage of finance and information technology.

The new revolutionary FinTech technology is turning the world into a unique biosphere, one with more straightforward transactions and higher security. An innovative FinTech solution is available whenever and wherever one has to undertake a transaction, for example, riding a cab, shopping at a small retail store, or any online transaction. In many ways, it is changing the financial sector; minimizing the financial sector’s costs, improving the quality of financial services, and helping organizations manage their finances efficiently with high security against cyber-attacks (Subbarao, 2017). There are many factors behind this rapid development of FinTech, the most prominent of these is the increased role of IT in our day-to-day activities. Phenomenons like cloud computing, the internet, and big data have made it possible for the financial sector to enhance processes, services, and business models. InsurTech, Robo advisory services, and crowdfunding platforms have changed the horizon of the insurance, investment, and financial sectors (Puschmann, 2017). Another factor contributing to the rise of FinTech is the use of a more focused and specialized approach in the insurance and financial industry. The process of restructuring and resizing critical financial sector operations and the inclusion of Fintech startups for these operations has led to the development of a new ecosystem. The recent corporate inclusion trend from outside the financial sectors like O2 Telefonica and the Fidor Bank is one example of the change mentioned above. Studies like Nüesch et al. (2015) have indicated the increased use of electronic channels by customers to be another prominent factor for the growth of FinTech. The increasing fusion of the digital and the physical world has led to the development of new customer interaction methods (Brenner et al., 2014). The frequency of the use of interaction channels by customers has increased in the last decade. Hybrid client interactions have been introduced by most financial service providers resulting in the widespread use of FinTech. Limited regulations and encouraging response to the launching of initiatives like FinTech “sandbox” are other factors contributing to the rapid growth of FinTech. The actions of regulators in London, Singapore and Hong Kong showcase the presence of a soft regulatory framework in this regard.

Various studies have emerged on the theoretical and practical aspects of financial technology. From the adoption and technology acceptance perspective, S. Singh et al. (2020) suggested a research framework by adding the substructures of the technology acceptance model. Lee and Shin (2018) discussed the components of the FinTech ecosystem as comprising of technology developers, startups, financial institutions, end-users and regulators. Similarly, the utility and involvement of FinTech are present in several other technology sectors, for example, crowdfunding (Ahlers et al., 2015; Belleflamme et al., 2014; Hervé & Schwienbacher, 2018), mobile payments, (Shankar & Datta, 2018; Verkijika, 2020), virtual currencies (Baur, Hong et al., 2018; Dwyer, 2015; Urquhart, 2016), blockchain technology (Ahluwalia et al., 2020; Dai & Vasarhelyi, 2017), and cloud computing (Park & Park, 2017).

The majority of the FinTech literature emerged after the year 2015 (see Figure 2). Consequently, in recent years, various books, papers, and conference papers have also analyzed the existing FinTech literature (Gai et al., 2018; Gomber et al., 2017; Palmié et al., 2020; Puschmann, 2017; Sangwan et al., 2019). However, previous bibliometric reviews have either not undertaken an exhaustive literature search (see Table 1), or they were limited by their topic selection. Most previous studies provide limited information regarding the influential institutes and researchers in different streams or key technology aspects of FinTech. These studies have also not comprehensively covered the potential future research directions in FinTech. Against this backdrop, this study attempts to contribute to the existing literature by providing a more extensive bibliometric review of FinTech research by covering a large number of articles not covered before. The study will attempt to answer the following questions (1) What are the main areas of FinTech research? (2) Which are the leading institutions, countries, journals, and authors in the domain? (3) Which are the most influential and trending articles and topics? What are the key research streams in the FinTech literature? (4) What are the further research questions? This study goes beyond the limited scope of previous bibliometric review studies on FinTech. The researchers have conducted a detailed bibliometric analysis of the existing literature, published between January 1996 to March 2020. We selected the WOS database for the study and 1,360 published papers were selected. A comprehensive bibliometric citation analysis, a burgeoning technique mostly used by the review papers in the domain of management and social sciences, was then conducted (Bahoo et al., 2020). The majority of the review papers in finance have been based on the conventional survey method, focusing on a particular topic or issue (Ballester et al., 2019; Deku et al., 2019; French & Vigne, 2019; Garner et al., 2016; Nguyen & Boateng, 2015). However, some recent review studies in finance have used bibliometric citation techniques, for example, Helbing (2019) for literature on IPO, Paltrinieri et al. (2019) for literature on Sukuk, and Paltrinieri et al. (2019) for credit risk literature.

Table 1.

Summary of FinTech Review Papers.

Tile/authors/years	Research objectives	Scope	Methodology (sample, technique, database, period)
A survey on FinTech	The objective of the study is to survey FinTech by collecting and reviewing contemporary achievements	Using the previous literature study summarized	Review of selected researches on the mentioned five technical aspects.
Gai et al. (2018)	To propose a theoretical data-driven FinTech framework	Five technical aspects of Fintech models.
Fintech	To discuss the evolution of FinTech	Summarize the existing literature on the evolution and dimensions of Fintech.	The review paper mainly focused on the evolution of Fintech
Puschmann (2017)	Terms and approaches used in FinTech
	To discuss dimensions of FinTech
The Evolution of FinTech:	The focus is on fintech evolution, the study divided fintech evolution eras into different parts.	Covers the articles on websites and newspapers	Exploratory review on FinTech evolution and regulatory innovation
A New Post-Crisis Paradigm?	Discussed its evolution in emerging markets.
D. W. Arner et al. (2015)	Overview of regulatory Innovation and the importance of RegTech
Financial technology: a review of extant literature	Thematic review of academic papers on financial technology (FinTech)	Covers 130 studies from SSRN, Scopus and other sources	It is a systematic review of literature that divides FinTech into three themes financial industry, innovation, and regulations
Sangwan et al. (2019)	Summarizes the existing literature and identify the research gap
The evolution of the financial technology ecosystem: An introduction and agenda for future research on disruptive innovations in ecosystems	The study discusses disruptive innovation and fintech ecosystems and explains the impact that the FinTech ecosystem has on disrupting the financial services industry.	Covers the views of	It is an exploratory and qualitative research based on the interview of industry experts.
Palmié et al. (2020)	Aim to give future research agenda about disruptive innovations and ecosystems	78 industry experts on their opinion about how
		FinTech disrupts the banking system

Note. The table summarizes the existing review papers on FinTech.

This study has multiple findings; The results of the study highlight the influential aspects of FinTech literature, including the top authors, institutions, countries, country collaborations, journals, and articles. It also identifies and discusses the main streams of FinTech literature; these include Bitcoin, Blockchain, Crowdfunding, and Mobile payment streams. Finally, the authors identify the questions for future research in the field using the content analysis technique.

The rest of the paper is structured as follows; section 2 presents the research methodology, section 3 discusses the influential aspects of FinTech literature, section 4 provides an overview of the research streams and section 5 presents future research directions. Lastly, the conclusion is presented in section 6.

Research Methodology

Glass (1976) described meta-analysis as “the analysis of analysis.” It is a combination of different analysis types and is an ever-growing domain covering various aspects of the previous studies. A bibliometric approach is used to map and analyze academic research. The significant advantage of bibliometrics probably resides in the fact that the information yielded is generally highly compact, easy to handle, and likely to be objective (Jokić & Ball, 2006). In comparison, bibliometric citation analysis is a method used to create a citation network among various studies focusing on a particular area of research. Citation analysis is a burgeoning technique used in management, finance, and business research (Zamore et al., 2018). This study utilized citation analysis along with content analysis of the existing literature to bring forward the most eminent facets of the different streams in the FinTech literature. Previous studies in the management and finance literature have used the same methodology to explore various research domains (Bahoo et al., 2020; Fetscherin et al., 2010; Gaur & Kumar, 2018; Paltrinieri et al., 2019).

Sample Selection Process

The sample selection process moved through three phases as shown in Figure 1. The first phase involved the selection of a database. For this purpose, the Web of Science database was chosen. The Clarivate Analytics’ Web of Science is a comprehensive and popular database for academic research studies encompassing high-quality scientific publications. It covers more than 12,000 multidisciplinary high impact factor journals and includes 55 social science disciplines (Analytics).

Figure 1.

Methodology flowchart.

The second phase involved a literature search of the database using keyword search criteria. The selection of appropriate keywords was crucial in this phase to ensure that the full body of literature was covered. The keyword base selected by J. Liu, Li et al. (2020), given the scope of earlier studies on the topic and the need for more comprehensive coverage in the current study. Further keywords like RegTech, PropTech, big data, Samsung pay, micropayment, sandbox, ICO, GovTech, altcoin and digital wallet were also added. The following search query was used:

TS = (fin-tech OR fintech OR “Fin tech” OR “financial technology*” OR “P2P lending platform” OR “Online lending” OR Crowdfund* OR “Transaction terminals” OR “Payment terminals” OR “Cashless payment*” OR Paypal OR Alipay OR Blockchain OR “Smart contract” OR “Digital currency” OR “Bitcoin” OR “Cryptocurrency” OR “Mobile payment” OR “Mobile point of sale” OR “Robo-advisors” OR “Automated portfolio management” OR E-bank* OR “mobile bank*” OR InsurTech OR micropyament* OR “micro payment*”)

One of the objectives of the extensive keyword query was to conduct an exhaustive search for relevant articles and to include those not covered by earlier studies. 28,153 papers were, thus retrieved in the pre-filtration phase.

The third phase of the sample selection process involved data filtration. After a keyword search, the retrieved sample must be assessed for authenticity and relevance. The filtration process ensures this in a study (Budgen & Brereton, 2006). Here it involved many steps. During the data filtration process, all studies from scientific fields not related to the area of research were excluded. Only studies related to the fields of economics, finance, business, and management were kept. Furthermore, papers with incomplete and missing information were excluded. Finally, following Zott et al. (2011), the authors ensured that all of the articles in the final selection were related to Fintech. Two independent authors contributed to the process to ensure that the articles were filtered according to the study criteria. The process resulted in 1,360 highly relevant records. These included 1,239 journal articles, 66 editorial materials, 45 review papers, 8 proceeding papers, 1 book chapter and 1 data paper. Book reviews and meeting abstracts were excluded.

The quantitative assessment (citation analysis) was conducted using HistCite and VosViewer software. Both are powerful tools for such analysis and have been used in previous studies (Alon et al., 2018; Bahoo et al., 2020; Helbing, 2019; Paltrinieri et al., 2019).

Meta-Literature Review

The meta literature review was conducted in multiple stages following Paltrinieri et al. (2019). These involved (i) identifying the influential aspects of FinTech Literature, (ii) conducting the co-citation and co-authorship analysis, and cartography, and (iii) conducting the content analysis. First, a citation analysis was conducted using the HistCite software to determine the influential aspects of the FinTech literature. The database search yielded 1,360 articles by 2,672 authors published in 424 different journals. A co-citation andco-authorship analysis was done using the VOSviewer. The distance-based mapping technique was used in the VOSviewer to visualize connections (van Eck & Waltman, 2010). Co-citation analysis was useful in the identification of streams and clusters in the selected literature. Similarly, co-authorship analysis helped identify the authors working in the same area of research. Cartography analysis was also conducted. It analyzes the network of reoccurring keywords obtained by using VOSviewer. The analysis is based on the assumption that keywords represent the content in an article, and similar keywords create a cluster in a field (Ding et al., 2001). The keywords in the cartography network represent the extensions of research surrounded by the main research domain.

Finally, a brief content analysis of the main streams in the literature was conducted using citation analysis. The dimensions and findings of these have been discussed in the identified streams. It is a useful technique to recognize and confirm bibliometric citation analysis; it also helps identify future research directions (Paltrinieri et al., 2019).

The Influential Aspects of the FinTech Literature

Annual Scientific Production

Figure 2 presents the year-wise publications, total local citations received per year (TLC/t), and total global citations received per year (TGC/t) trends in FinTech research between 2006 and 2019. The trend analysis shows that publications and citations have gradually increased over time, with most growth seen between the years 2017 and 2019. The most productive years were 2018 and 2019 with 299 and 499 publications, respectively. However, the maximum TLC/t (447) and TGC/t (1254) appeared in 2018.

Figure 2.

Yearly research Trend.

Top Influential Countries and Institutions

Although the FinTech domain is relatively new, the swift growth in FinTech literature in recent years is worth mentioning. Therefore, it is vital to identify the centers of excellence related to FinTech research in terms of countries and institutions.

Table 2, indicates the top 10 countries involved in FinTech research. We have identified top countries based on the number of papers published (P_FT > 50), total local citations, and (TLC) and total global citations (TGC). The country-wise analysis showed that the United States of America, the United Kingdom, China, Germany, France, Italy, and Canada were the most active countries in terms of publications and citations. As can be seen, they are listed among the top 10 in all three categories. The USA and the UK were the top two countries in all three categories of the list. The USA is ranked one in all three categories (PFT = 319, TCL = 2,099, TGC = 6,235), followed by UK (PFT = 183, TCL = 1,253, TGC = 3,242). China and Germany are ranked third and fourth rank with 164 and 117 publications, respectively; however, China received only 434 TCL and 1835 TGC. France, Italy, and Canada have produced 115, 64, and 55 publications and received 2,469, 912, 1,234 TGC each.

Table 2.

Most Influential Countries in FinTech Research (2006–June 2020).

Rank	Country^a	P_FT	Country^b	TLC	Country^c	TGC
1	USA	319	USA	2,099	USA	6,235
2	UK	183	UK	1,253	UK	3,242
3	China	164	France	977	France	2,469
4	Germany	117	Germany	902	Germany	2,464
5	France	115	Canada	518	China	1,835
6	Australia	72	China	434	Canada	1,234
7	Italy	64	Belgium	429	Belgium	1,131
8	Spain	59	Italy	399	Finland	1,026
9	Canada	55	Ireland	343	Australia	979
10	India	52	Lebanon	327	Italy	912

Note. The table shows the ranking of 10 influential countries based on various classifications.

Countries sorted based on the number of published papers in the period selected.

Countries sorted based on TLC.

Countries sorted based on TGC.

Figure 3 displays the top 20 country research collaboration map by Biblioshiny software based on the affiliation of authors involved in the Fintech research. The minimum edge parameter was taken as 5. The United States of America emerged as a top collaborator with the United Kingdom (31 publications), China (27 publications), France (19 publications), followed by China with the United Kingdom (18 publications), and France with Lebanon (17 publications) and others.

Figure 3.

Research collaboration.

Further, the top 10 organizations producing research publications on FinTech are shown in Table 3. The table shows the ranking of the top 10 most influential institutions based on the number of published papers (P_FT > 10) in the period selected as well as based on TLC. The study results showed that the University of Montpellier, France, Holy Spirit University, Lebanon, Southampton University, United Kingdom, University of Dublin, Ireland, and Politecnico di Milano, Italy, were the most active organizations in terms of publications and local citations. They were among the top 10 in both categories. The University of Montpellier, France, and the Holy Spirit University, Lebanon, were the two most successful and productive organizations in both categories of the list. As can be seen, the University of Montpellier is ranked first in both categories (PFT = 23, TLC = 385), followed by the Holy Spirit University (PFT = 17, TLC = 235). The Southampton University is ranked fifth (PFT = 13, TLC = 253), and the University of Dublin is ranked fourth rank (PFT = 11, TLC = 200) on the list. The Politecnico di Milano is ranked at the bottom of the list with 10 publications and 194 local citations.

Table 3.

Most influential Institutions in FinTech Research.

Rank	Institution^a	P_FT	Institution^b	TLC
1	University of Montpellier, France	23	University of Montpellier, France	385
2	Holy Spirit University, Lebanon	17	University of Pennsylvania	360
3	Southwestern University of Fin & Econ, China	14	Catholic University Louvain	253
4	Copenhagen Business School, Denmark	13	Southampton University	253
5	Southampton University	13	Holy Spirit University, Lebanon	235
6	University of Sydney	12	University of Lille 2, France	227
7	University of Dublin	11	University of Dublin	200
8	University of Pretoria, South Africa	11	Politecnico di Milano	194
9	University of Science & Technology China	11	York University	187
10	Politecnico di Milano	10	University of Oklahoma	170

Note. The table shows the ranking of 10 influential institutions producing research in the area of FinTech. TLC = total local citations.

Institutions sorted based on the number of published papers in the period selected.

Institutions sorted based, on TLC.

Table 4 shows the top 15 influential departments within institutions producing research in the area of FinTech. The list of departments/schools are sorted based on the number of published papers in the period selected. In the case of multiple departments with the same number of publications, priority was given to the department with the highest local citation score. The USEK Business School, the Holy Spirit University, the Montpellier Business School, the University of Montpellier, the Trinity College, Dublin, and the University of Dublin, remained the most productive Institutions. Such findings would be useable for other countries and institutions while deciding on collaborations on multiple research projects and organizing workshops on FinTech (Fetscherin & Heinrich, 2015).

Table 4.

Prominent Departments/Schools Within Most Influential Researching Institutions.

No.	Institution	Department/School^a	P_FT
1	University of Montpellier, France	Montpellier Business School	9
2	Holy Spirit University, Lebanon	USEK Business School	15
3	Southwestern University of Fin & Econ, China	School of Economics information & Engineering	6
4	Copenhagen Business School, Denmark	Department of Digitalization	8
5	Southampton University	Southampton Business School	8
6	University of Sydney	University of Sydney Business School	6
7	University of Dublin	Trinity Coll Dublin	9
8	University of Pretoria, South Africa	Dept of Economics	6
9	University of Science & Technology China	School of Management	8
10	Politecnico di Milano	Dept. of Management Eco. & Engineering	6
11	University of Pennsylvania	Wharton School	5
12	Catholic University Louvain	Louvain School Management	2
13	University of Lille 2	SKEMA Business School	1
14	York University	Schulich School Business	4
15	University of Oklahoma	Price College of Business	3

Note. The table shows the influential departments within institutions producing research in the area of FinTech.

Departments/schools sorted based on the number of published papers in the period selected. In the case of multiple departments with the same number of publications, priority is given to the department with the highest LCS (local citation score).

Most Influential Journals

The awareness regarding top journals and prominent authors in FinTech research would be beneficial for researchers working in the area. Within our main dataset, the 1,360 selected articles have been published in 424 journals related to finance, economics, management, and some multidisciplinary areas. These journals’ impact in terms of the number of publications, total local citations per year (TLC/t), and total global citations per year (TGC/t) are highlighted in Table 5. Three journals had over 40 publications. We also identified the top 10 most influential journals to promote their recognition. We determined the leading 10 journals in three categories based on the number of articles published, TLC/t, TGC/t received per year. The top three journals in the P_FT category included Finance research letters, Economics letters, Electronic commerce research, and applications. The Entrepreneurship Theory and practice and the Journal of business venturing also remained influential in TLC and TLG categories, respectively.

Table 5.

Most Influential Journals in Fintech Research.

Rank	Name of journal^a	P_FT	Name of journal^b	TLC/t	Name of journal^c	TGC/t
1	FRL	56	ELL	207.0	FRL	400.5
2	ELL	41	FRL	204.5	ELL	378.5
3	ECA	31	ETP	137.3	JOV	309.0
4	POO	29	JOV	110.2	ETP	274.4
5	TFC	29	SBE	52.7	ECA	162.9
6	FII	26	IRA	51.1	POO	133.8
7	JOM	25	JOIM	43.3	SBE	116.9
8	RIF	24	AEE	40.0	IRA	116.0
9	SCF	22	RIF	37.0	TFC	92.4
10	LEDGER	56	ECA	29.0	BHH	87.4

Note.The table shows the ranking of 10 influential journals based on various classifications.

journals sorted based on the number of published papers in the period selected,

journals sorted based on TLC/t.

journals sorted based on TGC/t.

Most Influential Authors and Co-authorship Networks

Table 6 highlights the top 10 influential authors/researchers in the FinTech literature. They are ranked based on the total local citations received per year (TLC/t). It can be seen that three authors had more than 10 publications each, but David Roubaud had the highest 113.5 TLC/t amongst all. Similarly, Elie Bouri, who is ranked second on the list, had 18 publications, 98.5 TLC/t and 327 TLC, followed by Andrew Urquhart with 62.9 TLC/t, 237 TLC and eight publications, and Ethan Mollick with 51.35 TLC/t, 339 TLC and three publications. Brian Lucey, who is at the bottom of the table, had eight Fintech publications and received 35 TLC/t, 93 TLC citations.

Table 6.

Most Influential Authors and Co-Authorship Networks in FinTech Research (2006–June 2020).

Rank	Name of author	Name of university/institution	P_FT	TLC	TLC/t
1	David Roubaud	Montpellier Business School, France	17	372	113.5
2	Elie Bouri	Holy Spirit University of Kaslik, Lebanon	18	327	98.5
3	Andrew Urquhart	University of Southampton, Southampton, UK	8	237	62.9
4	Ethan Mollick	University of Pennsylvania, United States	3	339	51.3
5	Rangan Gupta	University of Pretoria, South Africa	8	163	47.6
6	Peter Molnár	Norwegian University of Science and Technology, Norway	5	199	47.4
7	Armin Schwienbacher	Université Lille 2, France	10	268	45.7
8	Anne Haubo Dyhrberg	University College Dublin	4	214	44.7
9	Paul Belleflamme	Université catholique de Louvain, Germany	2	253	37.0
10	Brian Lucey	Trinity Business School, Trinity College Dublin, Ireland	8	93	35.0

Note. This table indicates the top 10 influential authors among all authors using HistCite software. The ranking is done based on the total local citations received per year (TLC/t).

The major co-authorship networks among authors working in the domain of FinTech were also investigated. The analysis was performed using the VOSviewer (Piette & Ross, 1992). For this purpose, criteria were set at a minimum of seven collaborative publications and a minimum of 50 citations. As indicated by Figure 4, we found four main clusters of authors working in collaboration. Table 7 highlights these co-authorship networks with key references. Such networks show research collaboration, which is a key mechanism that links distributed knowledge and competencies into novel ideas and research avenues (Heinze & Kuhlmann, 2008). Furthermore, co-authorship in research articles is considered a reliable proxy of research collaborations (Barnett et al., 1988).

Figure 4.

Co-authorship networks.

Table 7.

Co-Authorship Network.

No.	Color	Authors	Affiliation
1	Red	Elie Bouri	Holy Spirit University of Kaslik, Lebanon
		David Roubaud	Montpellier Business School, France
		Rangan Gupta	University of Pretoria, South Africa
2	Green	Jamal Bouoiyour	Université Pau France
		Refk Selmi	Université Pau France
		Aviral Kumar Tiwari	Rajagiri Business School, India
3	Blue	Brian Lucey	Trinity Business School, Trinity College Dublin, Ireland
3	Blue	Shaen Corbet	Dublin City University
4	Yellow	Walid Mensi	Sultan Qaboos University, Oman
4	Yellow	Khamis Al-Yahyaee	Sultan Qaboos University, Oman

Influential and Trending Articles

HistCite software was used to highlight the most influential and trending articles based on citations. Due to the growing nature of the literature in FinTech, such analysis might help researchers to set their future directions and research agenda. The influential articles included articles with high local citations per year TLC/t. The trending articles included the articles that have received high citations in the last period under selection TLC_e, which comprised of the last 3 years in our data.

There are three articles in this list that have received over 27 TLC/t and were top-ranked in both citation categories. These include articles by Mollick (2014), who attempted to explore the dynamics of success and failure among crowdfunding endeavors; Belleflamme et al. (2014), who compared two forms of crowdfunding using a theoretical framework; and Ahlers et al. (2015), who have empirically examined the effectiveness of signals on the success of equity crowdfunding. Interestingly all the top three articles were from the crowdfunding domain, indicating the popularity of the domain. Other prominent papers in the list include those by Urquhart (2016), Cheah and Fry (2015), and Bouri et al. (2017), which highlight the inefficiency of bitcoin, speculative bubbles in bitcoin pricing, and bitcoin as a diversifiable asset, respectively. It is worth mentioning here that bitcoin is the other major domain that remained the most influential and trending apart from Crowdfunding (Tables 8 and 9).

Table 8.

Ranking of the Top 10 Influential and Trending Articles and Topics in FinTech Literature.

Influential articles trending articles
Rank	Author (s) and year^a	Journal	TLC	TLC/t	Author (s) and year^b	Journal	TLC	TLCe
1	Mollick (2014)	JBV	301	43	Mollick (2014)	JBV	301	202
2	Belleflamme et al. (2014)	JBV	218	31.14	Belleflamme et al. (2014)	JBV	218	145
3	Ahlers et al. (2015)	ETP	164	27.33	Ahlers et al. (2015)	ETP	164	121
4	Urquhart (2016)	ECOLET	126	25.20	Cheah and Fry (2015)	ECOLET	113	103
5	Bouri et al. (2017)	FRL	92	23.00	Colombo et al. (2015)	ETP	123	90
6	Anne Haubo Dyhrberg (2016)	FRL	112	22.40	Agrawal et al. (2015)	JEMS	97	74
7	Colombo et al. (2015)	ETP	123	20.50	Böhme et al. (2015)	JEP	80	64
8	Urquhart (2016)	ECOLET	81	20.25	Dwyer (2015)	JFS	70	58
9	Baur, Hong et al. (2018)	JOIM	60	20.00	Cholakova and Clarysse (2015)	ETP	79	55
10	Katsiampa (2017)	ECOLET	77	19.25	Allison et al. (2015)	ETP	75	52

Note. This table represents the most influential and trending articles/topics.

The influential articles/topics are sorted based on TLC/t having a minimum number.

Trending is based on the criterion of a minimum TLC(e/b). the list of journals is provided in Appendix A.

Table 9.

Summary of Key Papers in Each Stream.

Study	Type of Study	Objective of the Study	Methods (technique/ /data)	Main Findings
Mollick (2014)	An exploratory empirical analysis of reward-based Crowdfunding	Presented initial evidence about the nature of Crowdfunding and its role in entrepreneurship research	Data taken from Kickstarter for 48,526 funding incidents covering the period from 2009 to 2012.	The majority of project founders using crowdfunding platforms fulfill their obligations to funders
				However, the majority of the product delivered to funders is delayed.
				Geography is also essential in the nature and success of the project.
Belleflammeet al. (2014)	Theoretical model derivation for two types of Crowdfunding	Theoretical framework development for pre-ordering versus profit-sharing Crowdfunding	Theoretical analysis with implications for the optimal design of crowdfunding initiatives.	Entrepreneurs prefer pre-ordering Crowdfunding if capital requirements are small. Whereas for more extensive capital requirements, they like a profit-sharing mechanism.
Belleflammeet al. (2014)	Theoretical model derivation for two types of Crowdfunding			Furthermore, entrepreneurial choice of Crowdfunding also depends on extra benefit.
Ahlerset al. (2015)	An empirical study on equity crowdfunding	Examine the impact of project attributes and signals and on the success of equity crowdfunding campaign.	Used data of 104 crowdfunding incidents from the Australian Small-Scale Offerings Board.	The study found that human capital has a significant impact on funding success.
Ahlerset al. (2015)	An empirical study on equity crowdfunding		The period used is October 2006 to October 2011.	The study also concluded that intellectual and social capital has no significant effect in fund raising campaign.
Lakhani and Iansiti (2017)	Descriptive study about Blockchain technology	A descriptive framework indicating the use and capabilities for the adoption of Blockchain in organizations.	Blockchain technology is discusses in the scenario of the current global digitalization of economies and business transformation.	TCP/IP has most likely smoothened the way of using blockchain technology.
				The executives must enhance their and staff capabilities for the adoption of technology.
				But there are risks and complexity in adopting technology that must be given due care by executives.
Dai and Vasarhelyi (2017)	Descriptive study about blockchain technology	The study attempts to explore the potential applications and utilization of the Blockchain in the	The study used different measurement and assurance paradigm for modern Blockchain and associated	The Blockchain-based accounting and assurance system will lead to real-time verifiable information disclosure
Dai and Vasarhelyi (2017)	Descriptive study about blockchain technology	accounting and auditing profession	smart contract technologies.	The development and implementation of the blockchain system in accounting and auditing domains will not be challenge free.
Urquhart(2016)	An empirical analysis of bitcoin efficiency	Using statistical bitcoin data, the study examines the market efficiency of bitcoin	The study used daily closing prices against USD for the period from 1st August 2010 to 31st July 2016.	For the entire length of the sample period, the inefficiency of Bitcoin remained quite strong.
Urquhart(2016)	An empirical analysis of bitcoin efficiency		Various statistical tests, including automatic variance test are applied to examine the Bitcoin efficiency.	However, for the subsample period, the Bitcoin returns exhibited some efficiency.
Bouri et al.(2017)	Empirical Study	Examine the possibility of bitcoin as a hedge for USD, gold, and global stocks.	Daily and weekly data have been used from July 2011 to December 2015.	Generally, the study concluded that Bitcoin is a low hedging asset against tested markets; however, it may only be used for diversification purposes.
Bouri et al.(2017)	Empirical Study		The study used a dynamic conditional correlation model for estimation	Bitcoin can be a hedge against weekly downward movements of Asian Stocks
Gomber et al. (2018)	Descriptive study	The study Evaluates the innovations, disruptions, and transformations in the Fintech Revolution	Examined and evaluated the continuous development in the FinTech domain and its future prospects	Larger firms will find it difficult to compete with small startups in Fintech innovation.
Gomber et al. (2018)	Descriptive study			IS researches have a more substantial potential of producing excellent and high-value FinTech in future
Schierz et al. (2010)	Empirical Study	The study attempts to examine the determinants of consumers’ acceptance of mobile payment services.	The structural equation model is applied to primary data collected through a questionnaire in Germany.	Perceived compatibility and individual’s mobility have the most significant impact on the intention to use mobile payment services

This table represents the most influential and trending articles/topics. 1 = The influential articles/topics are sorted based on TLC/t having a minimum number. 2 = Trending is based on the criterion of a minimum TLC(e/b). the list of journals is provided in Appendix A,

Keyword analysis

Figure 5 reveals the results of the cartography analysis conducted through the VOSviwer software. This type of analysis helps to identify the keywords under each research stream in a research area. The minimum selected scale of co-occurrence for a keyword was set at 20. Out of the 2,475 keywords used by authors, only196 met the threshold (criterion). The distance and size of the bubble define the number of keyword occurrences and associational links. These 196 keywords are associated with four main clusters. Each color represents the cluster with associational links among the keywords.

Figure 5.

Keyword analysis.

The largest cluster, in blue, represents the studies relating to bitcoin and digital currency. These studies discussed the topics relating to volatility, hedging properties, risk, and return relating to bitcoin. Whereas, the red color cluster represents the cluster of Crowdfunding articles associated with Kickstarter, peer to peer lending, equity and crowd investing, etc., comprise this cluster. The green cluster is the third-largest cluster and includes studies relating to Fintech, innovation, and mainly issues relating to blockchain technology. Similarly, the yellow color cluster highlights mobile payment, mobile banking, and other related studies.

Three-Factor Analysis (Organizations, Keywords, and Country)

Figure 3 presents a diagram of the published literature on Fintech focusing on the relationship among three factors, that is, top keywords, organizations, and countries. The top 10 keywords included Crowdfunding, entrepreneurship, Fintech, marketing, innovation, Blockchain, bitcoin, mobile payment, cryptocurrency, equity crowdfunding. These keywords seemed to have a relationship with 10 organizations, including the University of Minnesota; the University of California, Berkeley; the Southwestern University of Finance & Economics; the University of Science & Technology, China; the Perking University; the Copenhagen Business School; the University of Illinois at Urbana–Champaign; Montpellier Business School; and the Holy Spirit University of Kaslik. Furthermore, these organizations mainly had a strong relationship with 10 countries, that is, the United States of America, China, France, Italy, Germany, Canada, the United Kingdom, Australia, Spain, and India (Figure 6).

Figure 6.

Three-factor analysis.

Review of FinTech Research Streams

As discussed earlier, a co-citation analysis of the FinTech literature was also conducted in this study. Co-citation analysis is a kind of investigation intended to map out the linkages between the top-cited articles and the articles citing them (Alon et al., 2018). Following the studies of Alon et al. (2018) and Bahoo et al. (2020), we used HistCite to conduct this analysis. This type of analysis depends upon the size and the growth of the literature. We used a two-step strategy to perform the co-citation analysis. In the first step, we used the top 75 cited articles in the sub-samples with (TLC ≥ 20) and highlighted the main clusters of studies around one topic. The detail of the analysis is given in Figure 7 given below. The cited articles are positioned along the horizontal axis in the Figure, whereas the years are mentioned along the vertical axis. In the second step, two independent authors conducted the content analysis of the selected sample of articles. This analysis includes the review of title, author, journal data, methodology and key findings of the studies.

Figure 7.

Co-citation analysis.

This content analysis, along with the bibliometric analysis conducted in the previous section, enabled us to identify four main interrelated streams in the FinTech literature, that is, (1) the bitcoin and digital currency stream, (2) the crowdfunding stream, (3) the blockchain technology stream, and (4) the mobile payment technology stream. Further study of these streams revealed the interesting sub-streams available in the literature. Given below is a brief discussion on these four streams and sub-streams.

Bitcoin and Digital Currency Stream

The emergence of Bitcoin in 2013 is probably one of the most prominent events in the currency markets in recent years. Its launch by computer hobbyists has started a new era of a digital asset class. Since its inception, not only Bitcoin’s exchange rate has broken all the records, but a unique assortment of digital currencies have also flooded the market. Since the inception of the idea of Bitcoin idea by Satoshi Nakamoto, considerable debate has emerged on its different traits among academic, professional, and regulatory circles. A large number of articles in our selection also discussed the various features of Bitcoin. Topics like the volatility, return behavior, hedging and diversification properties of Bitcoin, its efficiency as an asset class, and its regulatory aspects have gained a lot of attention.

Market efficiency of Bitcoin

Urquhart (2016) found the Bitcoin market to be an inefficient one with a probability of moving toward a more efficient market. Similarly, Panagiotidis et al. (2019) explored the effects of variables like capital market returns, Federal Reserves and European Central Bank rates, gold, exchange rates, oil returns, and internet trends on bitcoin returns. The study observed that bitcoin enjoyed a strong interaction with traditional stock markets and had a weaker association with foreign exchange markets as well as the macroeconomy.

Volatility of Bitcoin

Besides, the volatility pattern of Bitcoin viz-a-viz other assets like gold, currencies, and oil is a significant concern. Anne Haubo Dyhrberg (2016) posited that using the scale from pure medium of exchange advantages to pure store of value advantages, bitcoin may fall between gold and the American dollar. Fang et al. (2019) showed that the economic policy uncertainty affects global equities, commodities and Bitcoin’s long-run volatilities. Moreover, Bitcoin also serves as a hedge against specific economic uncertainty conditions as global economic policy uncertainty exerts a significantly negative impact on Bitcoin-bonds correlation while positively and significantly influencing Bitcoin-equities and Bitcoin-commodities correlations. Köchling et al. (2020) assessed the quality of Bitcoin volatility forecasting of GARCH-type models by applying different volatility proxies and loss functions by constructing model confidence sets. They found these models to be systematically smaller for asymmetric loss functions and a robust jump proxy.

Bitcoin returns

It is also important to examine issues related to the predictors of bitcoin returns and the uncertainties involved in achieving bitcoin returns. Smales (2020) observed that a single principal component explained a substantial proportion of cryptocurrency returns variation. This component is highly correlated with bitcoin returns and exhibits the greatest explanatory power for larger cryptocurrencies. Baur et al. (2019) observed time-specific anomalies in Bitcoin returns but no persistent effects across time by investigating intra-day, time-of-day, day-of-week, and month-of-year effects for Bitcoin returns and trading volume. In comparison, they observed persistent differences in trading activity across all exchanges experiencing lower activity during local evening hours and on weekends.

Hedging and diversification properties of bitcoin

It is worth mentioning that bitcoin can be effectively used for hedging. Hussain Shahzad et al. (2020) reported that bitcoin had hedging properties, however, gold was a superior hedging instrument as compared to bitcoin. Ehlers and Gauer (2019) found that Bitcoin, Ethereum, Dash, CAD, JPY, and EUR contributed toward the reduction of the variance of a mixed portfolio. Moreover, in a portfolio comprising of only cryptocurrencies, Bitcoin and Ripple exhibited the largest diversification effect. Damianov and Elsayed (2020) observed that Bitcoin was relatively isolated from traditional industries and showed a near-zero correlation with traditional financial assets. Although it provided investors with some diversification benefits, these benefits are counterbalanced by the asset’s volatility.

Moreover, in terms of the liquidity and diversification benefits of Bitcoins, Anne H Dyhrberg et al. (2018) found that, for retail size transactions, Bitcoin was highly investible as the average quoted and effective spreads for Bitcoin were found to be lower than spreads on major equity exchanges. Baur, Dimpfl et al. (2018) demonstrated that Bitcoin offered distinctively different returns, volatility, and correlation characteristics than other assets like the US dollar and gold.

Crowdfunding Stream

One of the most critical needs of all new projects is the initial capital requirement. Crowdfunding is an innovative mechanism for fulfilling the financing needs of new for-profit, social, and cultural projects. The initial founders of such ventures may request funds from local or global contributors and the return of the contribution can be in the form of equity or future product (Mollick, 2014). The size and the objectives of the crowdfunding ventures vary. These may include individual skill-based projects with small seed capital requirements to the projects that require millions of dollars as initial requirements for which traditional venture capitalists were used as funding sources. Although Crowdfunding resembles concepts like micro-finance and crowding sourcing, it still has its uniqueness, with a growing number of websites developed for different types of Crowdfunding.

Equity crowdfunding

There are several kinds of crowdfunds. To begin with, equity crowdfunding (ECF) provides entrepreneurs with an online social media marketplace which facilitates their access to numerous potential investors who are in a position to supply financing for projects in exchange for an ownership stake. Estrin et al. (2018) observed that ECF platforms were instrumental in channelizing large financial flows to entrepreneurs in the UK. Such investments are deemed as high risk with high returns. Ahlers et al. (2015) empirically examined the effectiveness of signals provided by entrepreneurs to motivate investors for a funding commitment to investigate the success factors for an Equity Crowdfunding campaign. The study concluded that detailed information about the venture’s inherent risks would be a useful signal with the potential to make the campaign successful.

Entrepreneurial aspect of crowdfunding

Since the traditional financing sources have remained subpar for many types of social entrepreneurial ventures, Crowdfunding has attracted the attention of entrepreneurs in this regard. Websites like Kickstarter have proved to be vital sources of funding for a variety of entrepreneurs, especially for social entrepreneurs. Parhankangas and Renko (2017) examined the linguistic style of crowdfunding campaigns and concluded that a more relevant and logical linguistic style was important for a campaign’s success. Amedomar and Spers (2018) showed that Technology-Based Companies selected reward-based Crowdfunding as a funding model. In their early developmental stages, they required funds for financing specific projects and exhibited motivations not directly involving the raising of funds.

Reward-based crowdfunding

Contributors of the capital in a reward-based Crowdfunding campaign receive non-monetary benefits as a return. It may include pre-ordering of product, an acknowledgment, credit for a game, etc. Entrepreneurs have raised a considerable amount of money by posting their projects on reward-based crowdfunding websites. The role of incentives and pledges has been discussed by Crosetto and Regner (2018), who posited that the project’s success is only partially path-dependent as the bulk of projects eventually succeeding do not follow a successful track for 75% of their funding period. Although early pledges may predict project success, it does not necessarily imply that their absence will result in failure.

Moreover, the role of geography in Crowdfunding’s success has been discussed by Giudici et al. (2018), who showed that certain salient features of the geographical area, where entrepreneurs performed the business activity, influenced the crowdfunding success projects they proposed. In this respect, people’s altruism in the vicinity enhances the probability of success, depending upon the area’s level of social capital.

Peer to peer lending

In recent times, Peer-to-peer (P2P) lending has surfaced as a network form of Crowdfunding facilitating loan originations outside the traditional banking model. This platform provides anonymous connectivity between borrowers and lenders. It encourages both borrowers and lenders by providing a source of funding and investment outside the orthodox intermediary circles (Barasinska & Schäfer, 2014). Studies like Nisar et al. (2020) have examined the known risks to Chinese P2P companies. The regression model results indicate several factors such as marriage, income, and house property that could predict risk and financial management.

Mobile Payment Stream

In recent years, mobile phone devices surpass routine communication requirements. Now more and more value-added services, including mobile commerce and payments are outdoing the regular mobile uses. Moreover, the mobile phone is the most used device for product sale and purchase, product delivery and other e-commerce services (Dahlberg et al., 2008). Mobile payment is one such function of the, smartphone, which has witnessed remarkable growth in recent years. The payment function of mobile phones covers a variety of scenarios for example, payment of utility bills, money transfer, payments at the point of sales etc. This growing and novel use of mobile payment is no doubt very innovative. This innovation has been undoubtedly an important attribute behind the success of mobile payments. Zhong and Nieminen (2015) found that inter-organizational co-innovation was a successful strategy for mobile payment service innovation.

Trust of mobile payments

The trust of mobile payments is an important issue. N. Singh and Sinha (2020) measured the influence of variables like perceived usefulness, perceived compatibility, perceived cost, awareness, perceived customer value addition, and perceived trust on a merchant’s intention to use a mobile wallet technology. Their findings revealed that perceived customer value addition had the highest effect on merchants’ intention, followed by the perceived usefulness of the technology. The perceived trust showed a small but significant mediating effect on the perceived usefulness. Shao et al. (2019) investigated the differences between female and male consumers in respect to several trust-building mechanisms in the context of mobile payments. They found security to be the most significant antecedent of customers’ trust, followed by platform reputation, mobility and customization. For the male customers, mobility and reputation had stronger influences in building trust. On the other hand, for the female customers, security and customization had more pronounced effects on trust.

Adoption and acceptance of mobile payments

In terms of the adoption of mobile payments, Humbani and Wiese (2019) assessed the adoption and the intention to continue the use of mobile payment applications. They found the “drivers” better explained adoption as compared to the “inhibitors,” whereas satisfaction emerged as the strongest predictor of continuance intentions. de Luna et al. (2019), however, indicated that mobile payment behavior could not be applied globally.

Gupta and Arora (2019) observed that performance expectancy, effort expectancy, habit, and facilitating conditions were significant predictors of behavioral intention that, in turn, significantly predicted the use behavior regarding the use of mobile payment systems. Moreover, both social influence and hedonic motivation were found to be weak predictors of behavioral intention.

Blockchain Stream

A blockchain is an Internet-based peer-to-peer system constituting an integrated framework of independent and connected computers capable of simultaneously recording and verifying transactions. It improves business efficiency, lowers costs, enhances transparency, and provides a comprehensive history of all transactions. Nevertheless, it carries certain risks like data security issues, third-party vendor risks, technological risks, and interoperability risks. There were around 270 papers in the selected database that discussed various themes related to Blockchain.

The concerns about Blockchain

The security concerns are an essential caveat in the growth of Blockchain. White et al. (2020) emphasized that auditors who were advising clients on blockchain technology should possess the relevant knowledge and expertise of the technology and have the capability to assess the risks inherent in it. Park and Park (2017) focused on the security concerns related to the blockchain technology being used by governments. The study found that the security risk could be reduced if 10 fundamental objectives like data privacy, transparency, integrity, cost, etc., were addressed properly. Moreover, key strengths and weaknesses of blockchain technology have been reported by Babich and Hilary (2020). The key strengths identified were aggregation, visibility, automation, validation, and resiliency. On the other hand, the lack of standardization, black box effect, lack of privacy, garbage in/ garbage out, and inefficiency were highlighted as weaknesses of the technology.

Blockchain and smart contracts

The emergence of Blockchain technology has facilitated the debate on smart contracts. One key element of this debate is the idea of decentralized consensus, which is a technique for large network participants to arrive at a perfect agreement on a shared document. Since blockchain technology enlarges the contracting space and has the characteristics of cryptographical algorithms and decentralized consensus, it facilitates the emergence of smart contracts (Cong & He, 2019). By virtue of enhanced entry and competition, smart contracts may remove information asymmetry and improve welfare and consumer surplus. However, the distribution of information during consensus generation could promote more significant collusion.

Blockchain and the accounting profession

Although the scope of blockchain technology was initially limited to recording cryptocurrency transactions, over time, its application has increased in banking and finance, financial markets, electoral voting systems, the insurance sector and the accounting and audit sector (Dai & Vasarhelyi, 2017). Yu et al. (2018) discussed the application of Blockchain in financial accounting and its possible effects. They suggested that, in the short run, public Blockchain could be used as a platform for firms to disclose information voluntarily. It could effectively reduce disclosure and earnings management errors, improve the quality of accounting information, and mitigate information asymmetry in the long run. Furthermore, through an accurate and timely automatic assurance system, technology could become convenient in the audit profession (Dai & Vasarhelyi, 2017). Schmitz and Leoni (2019) listed themes like transparency, trust issues and governance in the blockchain ecosystem to argue that accounting and auditing have distinguished businesses’ requirements based on Blockchain.

Future Research Directions

The various analyses conducted in the study, including the citation analysis, the examination of influential aspects, and the investigation into the contents of the major streams, have enabled us to highlight future research directions in the Fintech literature. This future research agenda is presented in Table 10.

Table 10.

Future Research Questions.

Stream	Future Questions	Study
Bitcoin Stream	1. What are the diversification properties of bitcoin in a time-varying framework?	Bedi and Nashier (2020)
	2. What may be bitcoin return’s behavior with particular reference to emerging regulatory frameworks and policy changes about digital currencies?	Flori (2019)
	3. What will be the results of using advanced portfolio techniques other than multivariate techniques?
	4. What may be the effect of using various combinations of different cryptocurrencies as portfolios and a relatively long holding period?
Crowdfunding Stream	5. What are the motives other than the intrinsic and extrinsic motivations by Crowdfunding donors in charity based Crowdfunding?	Bagheri et al. (2019)
	6. What is the potential relationship between the composition of social networks and the success of the crowdfunding campaign?	Hervé and Schwienbacher (2018)
	7. What type of Crowdfunding is more useful to promote innovation in the economy?
	8. What is the impact of the regulatory environment of equity crowdfunding on entrepreneurial innovation?
Mobile Payment Stream	9. Is there any causality between the retailer’s decision to adopt a particular mobile payment platform and its business model innovation?	Jocevski et al. (2020)
	10. If emotions like effect and anticipated regret are used in combination with trust, security, and risk, how will it affect mobile payments’ adoption behavior?	Verkijika (2020)
	11. Apart from user-centric factors, how the factors relating to other stakeholders affect the adoption intentions of mobile payments?	Shankar and Datta (2018)
	12. How the education level, age factor, and gender impact the decision to use mobile payment? Previous studies have contradictory findings of these factors, so further exploration in different cultures is required on the topic?	Y. Liu, Wang, Huang, et al. (2019)
Blockchain Stream	13. What is the impact of cost minimization factors on startup investment decisions from an entrepreneurial point of view?	Ahluwalia et al. (2020)
	14. What is the effect of using smart contracts on the audit process, and will this use make the audit process more efficient?	Schmitz and Leoni (2019)
	15. Are all types of accounting information stored on blockchains, or is it better to store some specific kind of information?

The various aspects of Bitcoin technology have been well researched in the last 10 years, however, certain areas still require further investigation. In recent years, Bitcoin has remained very popular in the investment community. Researchers have also focused their attention on the investment and diversification properties of Bitcoin. Bedi and Nashier (2020) used the VAR and variance approach to examine the diversification properties of Bitcoin combined with five major fiat currencies. However, the authors recommended that future researchers might test Bitcoin’s diversification characteristics in a time-varying framework. Moreover, it is pertinent to observe Bitcoin’s combination with other asset classes in the scenario of regional financial markets in different economic conditions. Another exciting aspect for future research might be the investigation of Bitcoin return behavior in the different regulatory frameworks. Since the FinTech regulations in various parts of the world are still in the evolving phase, the Bitcoin return behavior would be exciting to investigate with the emergence of an ever-changing regulatory setup. Similarly, Flori (2019) suggested some interesting dimensions of future research including the use of advanced portfolio techniques other than the commonly used multivariate methods in the case of bitcoin investment. Furthermore, the study suggested using various combinations of different cryptocurrencies as portfolios; this testing should be done for a relatively long holding period.

The biggest challenge faced by any new venture is to attract funding sources. Crowdfunding is one source of funds that has gained immense popularity in recent times. The research circles have also investigated various aspects of Crowdfunding. However, there are certain aspects of Crowdfunding that require further exploration. Studies like Bagheri et al. (2019) have analyzed the motivations of donors in charity-based Crowdfunding. The study suggested exploring the motivations other than intrinsic and extrinsic motivations by Crowdfunding donors in charity based Crowdfunding campaigns. Moreover, further studies might be conducted to ascertain the linkages between the various features of the crowdfunding platform and the success of the campaign.

Finance and ﬁnancial institutions are important for the economy because they not only play a role in the selection of investment opportunities and the allocation of resources but also the ﬁnancing of innovation (Schumpeter, 1912). The function of selecting the best projects is crucial for the economy because innovation is an important source of growth. Since innovation is critical for the overall development of any economy, innovative ideas need necessary funding from both traditional and non-traditional funding sources. Crowdfunding platforms have been proven to be very useful in boosting entrepreneurial innovation. Furthermore, these platforms also allow providers of the fund to participate in the innovation process. In this regard, certain research queries require further attention from researchers. These include, what type of Crowdfunding is more useful to promote innovation in the economy? How is the regulatory environment of equity crowdfunding on entrepreneurial innovation?

Regarding mobile payments, we suggest an investigation into the relationship between the retailer’s decision to adopt a particular mobile payment platform and the innovativeness of its business idea. Similarly, an inquiry should also be made into the factors relating to other stakeholders’ adoption intentions of mobile payments?

Blockchain or distributed ledger technology, which was once known only as Bitcoin’s underlying technology, is now emerging as a disruptive innovation. Its application and usage are penetrating all businesses and even many other domains. It may be potentially able to redesign our interactions in business, politics, and society at large. Concerning Blockchain’s accounting and auditing applications, we suggest an investigation on the effect of using smart contracts during the audit process and how far would this usage make the audit process more efficient? Another important agenda for future research could be exploring the types of accounting information that might be stored on blockchains. Could it be any specific kind of information, or would it be acceptable to store all data types?

Conclusion

An extensive review of the Fintech literature was conducted in this study. The Web of Science Database was selected for data extraction and the initial keywords search yielded 28,153 articles. The final analysis was conducted on 1,360 articles obtained after excluding irrelevant papers. These articles had been published between the year 2006 and June 2020. Biometric Software like HistCite and VOSviewer were used to conduct the data analyses. As a part of the meta literature review, the following types of analyses were undertaken in this study (i) identification of the influential aspects of FinTech literature, (ii) co-citation analysis, (iii) co-authorship analysis, (iv) keyword analysis, (v) content analysis of major streams available in the literature, and (vi) identification of the future directions.

Although there have been many previous reviews available on FinTech, every new study adds value to the existing literature due to the growing nature of the literature. Furthermore, this study might be useful for future researchers as it provides an overview of significant streams and sub-streams available in the FinTech literature. These streams include (i) Bitcoin and digital currency stream, (ii) the crowdfunding stream, (iii) the mobile payment stream, and (iv) the blockchain stream. The study results also identify the main contributors, institutions, primary sources, and influential and trending articles related to FinTech. Future research direction has also been discussed.

Regarding the study’s utility, the updated information about various researchers and institutions would be useful for Fintech professionals and new startups for future collaborations, testing, and upgrading of ideas across multiple business stages. For young academic researchers, the information about main streams and sub-streams might provide useful insights to initiate future research work. Furthermore, such information could be useful for future networking opportunities among researchers and other interested stakeholders.

No study is without limitations and as such this research study was also limited by several factors. Firstly, we have only used the Web of Science as a database to extract the relevant studies. This was because of the limitation inherent to the leading software used for analysis; HistCite, only processes WOS data. FinTech is a growing domain, and new research available in other databases as well such as Scopus and Google scholar has been missed. So any future research may combine the data from all these databases to conduct a more comprehensive study. Another potential limitation is related to the citation analysis. In Citation analysis, we rate studies, authors, and institutions based on the number of citations received. However, some recent good quality articles did not have many citations because of their recent date of publication and were ignored by the analytical software used. These articles would probably gain high citations in the future. So any future research using the same methodology may highlight these articles.

This research was conducted while Dr. Ishtiaq Ahmad Bajwa and Dr. Zaheer Anwer were at Imam Abdulrahman Bin Faisal University, Dammam, and the University of Management and Technology, Lahore, respectively. They are now at Beaconhouse National University, Lahore, and Sunway University, Malaysia and may be contacted at Ishtiaq.bajwa@bnu.edu.pk and zaheerm@sunway.edu.my.

Footnotes

Appendix

Appendix.

Abbreviations Used for Journals.

Abbreviation	Name of journal	Abbreviation	Name of journal
FRL	Finance research letters	JBV	Journal of business venturing
ECOLET	Economics letters	SBE	Small business economics
ECA	Electronic commerce research and applications	IRA	International review of financial analysis
POO	Plos one	ETP	Entrepreneurship theory and practice
TFC	Technological forecasting and social change	AEE	Applied economics
FII	Financial innovation	RIF	Research in international business and finance
JOM	Journal of risk and financial management	BHH	Business horizons
RIF	Research in international business and finance	LEDGER	Ledger
SCF	Strategic change-briefings in entrepreneurial finance	JOIM	Journal of international financial markets institutions & money
JEMS	Journal of economics & management strategy	JEP	Journal of Economic perspectives
JFS	Journal of financial stability

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Ishtiaq Ahmad Bajwa

Shafiq Ur Rehman

Abid Iqbal

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Past,Present and Future of FinTech Research: A Bibliometric Analysis

Abstract

Keywords

Introduction

Research Methodology

Sample Selection Process

Meta-Literature Review

The Influential Aspects of the FinTech Literature

Annual Scientific Production

Top Influential Countries and Institutions

Most Influential Journals

Most Influential Authors and Co-authorship Networks

Influential and Trending Articles

Keyword analysis

Three-Factor Analysis (Organizations, Keywords, and Country)

Review of FinTech Research Streams

Bitcoin and Digital Currency Stream

Market efficiency of Bitcoin

Volatility of Bitcoin

Bitcoin returns

Hedging and diversification properties of bitcoin

Crowdfunding Stream

Equity crowdfunding

Entrepreneurial aspect of crowdfunding

Reward-based crowdfunding

Peer to peer lending

Mobile Payment Stream

Trust of mobile payments

Adoption and acceptance of mobile payments

Blockchain Stream

The concerns about Blockchain

Blockchain and smart contracts

Blockchain and the accounting profession

Future Research Directions

Conclusion

Footnotes

Appendix

Declaration of Conflicting Interests

Funding

ORCID iDs

References