Fintech: A content analysis of the finance and information systems literature

Abstract

The amount of research related to financial technologies (fintech) has grown rapidly since these modalities have been implemented. A review of this literature base will help identify the topics that have been explored and identify topics for further research. This research project collects, synthesizes, and analyzes both the research strategies (i.e., methodologies) and content (e.g., topics, focus, categories) of the literature, and then discusses an agenda for future research efforts. We searched for fintech research published in the last 20 years and analyzed 146 articles published in Finance and 70 articles published in Information Systems (IS) during this period in their respective A*, A, and B journals in the 2019 Australian Business Deans Council list. We found an increasing level of activity during the most recent 6-year period and a biased distribution of fintech articles focused on exploratory methodologies. We also found several research strategies that were either underrepresented or absent from the pool of fintech research and identified several subject areas that need further exploration. We also created four fintech topic categories to organize and classify this diverse research stream.

Introduction

With the continuous advancements in technology, the current interest in fintech in both academia and in practice is more prevalent than ever. Typically, a portmanteau for “financial technology”, fintech has been referenced for more than 40 years in more than 200 scholarly articles (Schueffel, 2016). Throughout the years, different definitions of fintech have been proposed for different contexts and across countries, while the origin of the term “fintech” remains to be a point of contention. Only until recently, Schueffel (2016) reconciles various existing definitions and defines fintech as “a new financial industry that applies technology to improve financial activities.” As a joint evolution of finance and technology, fintech encompasses cryptocurrencies, Internet banking, mobile payments, crowdfunding, peer-to-peer lending, Robo-Advisory, online identification, and many other important innovations (Lagna & Ravishankar, 2022). Nonetheless, fintech is still a relatively undiscovered academic field and expects its definition to continue to evolve. To date, no study has examined neither the methodologies employed nor the content thereof. The purpose of this study is to synthesize the methodologies and content of all fintech article from the past 20 year encompassing all journals on the Australian Business Deans list that have a rating of A*, A, and B. In doing so, we hope to find a synthesis of keywords and methodological advances that can be used in further exploration of fintech research.

Studies which systematically review the literature, such as Farooq and Jibran (2018), have been shown to be valuable contributions to understanding the scope, measurements, impact size, and determinants of a particular area to synthesize with the area’s future research agenda. In this paper, we performed a meta-analysis of research methods employed in the data stream of fintech research. In the literature stream, there has not been a comprehensive survey of the methodologies employed in fintech literature. In fact, there have been very few studies reviewing the methodologies employed in finance research in the past 15 years, with Kim and Ji (2015) and Adams et al. (2019) being the closest examples. Lagna and Ravishankar (2022) illustrated the growing interest that IS researchers have shown in the fintech research domain. Alt et al. (2018) called fintech a revolution that had evolved from offline, hierarchical, process-oriented organizations to digital, agile, customer-centric system and stated, “FinTech businesses are more IT companies than financial providers were before.”

The following sections of the paper will examine the current literature to determine what is known about the concept of fintech. The remainder of this paper is organized as follows: a description of the methodology for the analysis of the fintech research is presented. This is followed by the results. Finally, the research is summarized with a discussion of the limitations of this project and suggestions for future research.

Literature review

One focus in the fintech literature is about how fintech companies provide new and improved financial services. As Thakor (1999) discusses, the development of information technology enables new financial firms to be highly specialized and provides products and services which are tailored to customer preferences. As new players in the financial market, fintech companies have the potential to reduce financial contracting frictions and increase consumer welfare (Philippon, 2015). For example, Fuster et al. (2019) find evidence that fintechs have improved the productivity of mortgage lending.

These additional values which fintechs may bring to the finance industry come from the fact that these firms are different from traditional financial institutions. Thakor (2020) discusses that fintech firms bare lower operating costs than traditional banks. For instance, Lending Club, a fintech firm, has operating costs as a percentage of outstanding loans at 2.70% compared to those of banks at almost 7%. According to Benoit et al. (2019), fintechs also have lower regulatory costs than banks. In the USA, even though peer-to-peer (P2P) lending is subject to the US Securities and Exchange Commission (SEC)’s regulation and state laws, these regulatory burdens are much lighter than that of banks.

Much of the recent fintech research is concerned with how fintechs impact traditional banks. Christensen (2016) provides the “disruptive theory” in which new entrants effectively compete with traditional players by providing accessible and cost-effective goods and services to customers. Boyd and De Nicolo (2005) posit that banks become more competitive by providing cheaper loans. In turn, borrowers have less incentive to risk shift which results in banks having less default risk. Similarly, Goetz (2018) finds that the increased competition forces banks to be more efficient by reducing over-lending and engaging in relationship lending. On the other hand, Bertsch et al. (2020) find that banks’ increased misconduct is related to the emergence of the US online lending market. Large banks can also choose to acquire fintech firms. For instance, in 2015, Capital One acquired Level Money to strengthen its capabilities in digital banking technologies (Li et al., 2017). Hornuf et al. (2021) find that many banks acknowledge the technical superiority of fintech start-ups and have incorporated these firms’ products and services into their own business models.

Thakor (2020) and other survey papers review the fintech literature’s research contents of what we currently know about fintech and the research directions that have been taken. On the other hand, this paper focuses on reviewing the research methodologies. Studies which systematically review the literature, such as Farooq and Jibran (2018), have been shown to be valuable contributions to understanding the scope, measurements, impact size, and determinants of a particular area in order to synthesize with the area’s future research agenda. There has not been a comprehensive survey of the methodologies employed in fintech literature. In fact, there have been very few studies reviewing the methodologies employed in finance research in the past 15 years, with Kim and Ji (2015) and Adams et al. (2019) being the closest examples.

For the purpose of reviewing the practice of significance testing, Kim and Ji (2015) survey recently published articles in four top-ranking finance journals. They find that finance researchers almost exclusively use the conventional significance levels (1%, 5%, and 10%) while paying little attention to the sample size, power of the test, and expected losses. The authors also suggest using more often the Bayesian method or revised standards for evidence (0.1% or 0.5%). Adams et al. (2019) review the articles published in the same four top-ranking finance journals from 1988 to 2017 in order to investigate whether outliers are treated appropriately in these studies. The authors document that each year, 30–70% of these articles use OLS. To encourage finance researchers to utilize other useful econometric methods, they propose a multivariate outlier identification strategy. As the authors explain, this technique can minimize frictions which hinder the adoption of these methods. Due to their purposes of addressing very specific problems, these two articles provide method surveys that are non-comprehensive. Table 1 summarizes the differences between this paper and the other surveys of fintech methods.

Table 1 The surveys of fintech methods

Methodology

The approach to the analysis of the fintech research is to first identify trends in the Finance and Information Systems (IS) literature because fintech is the intersection between financial services and information systems. Specifically, we wished to capture the trends pertaining to (1) the number and distribution of fintech articles published in the leading journals, (2) methodologies employed in fintech research, and (3) the research topics being published in this research. During the analysis of this literature, we attempted to identify gaps and needs in the research and therefore enumerate and discuss a research agenda which allows for the progression of research (Webster & Watson, 2002). Systematic literature reviews are a meta-analysis technique designed to collect, organize, analyze, and categorize existing knowledge and concepts in the research literature of a given category (Briner et al., 2009). In short, we hope to paint a representative landscape of the current fintech literature base to influence the direction of future research efforts in this important area of study.

To examine the current state of research on fintech in the top Finance and IS journals, the authors conducted a literature review and analysis in three phases. Phase 1 accumulated a representative pool of articles. Phase 2 classified the articles by research method. Phase 3 classified the research by topic. Each of the three phases is discussed in the following paragraphs.

Phase 1: Accumulation of article pool

We used the Web of Science (WoS) citation database, Scopus citation database, and Google Scholar to search for research articles with a focus on fintech. The search parameters were constrained based on (a) a list of top ranked journals, (b) a specific time range, and (c) key search terms. Figure 1 illustrates steps in the content analysis process adapted from Neuendorf (2002) and successfully employed by several similar research studies in Internet marketing (Corley et al., 2013), Business Intelligence (Jourdan et al., 2008), and Enterprise Resource Planning systems (Cumbie et al., 2005).

Fig. 1

Overview of literature analysis

First, the researchers chose to use the journals from the Australian Business Dean’s Council ABDC list (ABDC, 2019). Then, we filtered the ranking of journals to include only Finance (Code 1502) and collected the list of A* (see Table 2), A (see Table 3), and B (see Table 4) journals. Then, we filtered the ranking of journals to include only Information Systems (Code 0806) and collected the list of A* (see Table 5), A (see Table 6), and B (see Table 7) journals. Many of the Finance and IS journals in the sample contained no fintech articles and were deleted from the tables.

Table 2 Number of fintech articles in A* Finance journals

Table 3 Number of fintech articles in A Finance journals

Table 4 Number of fintech articles in B Finance journals

Table 5 Number of fintech articles in A* Information Systems journals

Table 6 Number of fintech articles in A Information Systems journals

Table 7 Number of fintech articles in B Information Systems journals

The search parameters were further constrained to a specific timeframe. As previously discussed, the term fintech was first coined by Citicorp in 1993 (Schueffel, 2016). The search parameters were further constrained based on the historical timeframe in which technologies capable of facilitating the Finance function were first introduced, and the years of publications for articles in our search sample were constrained to the years of 2002 through December of 2021.

The final constraint was based on the key search term “fintech.” In WoS, Scopus, and Google Scholar the search engine scanned for the term “fintech” and close variations of this term found in the title, abstract, and keywords of articles published in the top Finance journals between January of 2002 and December of 2021 when the search was executed. There was a considerable overlap in the pool of articles returned from the three search engines (WoS, SCOPUS, and Google Scholar). Of the 322 (227 Finance and 95 IS) total articles in the initial search, 83 articles (67 Finance and 16 IS) were removed because the articles’ publication year was 2022. This further shows the explosive growth of this research topic’s popularity as the search was conducted in late February of 2022. Once duplicate entries and non-research articles (book reviews, editorials, commentary, etc.) were removed, another 26 (17 Finance and 9 IS) articles were removed. As a result of this process, 216 (146 Finance and 70 IS) articles remained in the composite data pool for analysis. All 216 article files were collected in Adobe Acrobat PDF format and loaded into NVivo 11 to run a word frequency query of the content without numbers and extemporaneous words (i.e. “a,” “and,” “the,” etc.). Figure 2 shows the word cloud that resulted from this query.

Fig. 2

Word cloud of fintech research created in NVivo

Phase 2: Classification by research strategy

Once the researchers identified the articles for the final data pool, each article was examined and categorized according to its research strategy. Due to the subjective nature of research strategy classification, content analysis methods were used for the categorization process (Neuendorf, 2017).

First, the research categories were adopted from Scandura and Williams (2000) (see Table 8), who extended the research strategies initially described by McGrath (1982). Specifically, nine categories of business research strategies were selected including: Formal theory/literature reviews, sample survey, laboratory experiment, experimental simulation, field study (primary data), field study (secondary data), field experiment, judgment task, and computer simulation.

Table 8 Fintech research strategies fromScandura and Williams (2000)

Second, to guard against the threats to reliability (Neuendorf, 2017), we performed a pilot test on articles not included in the final data pool for this study. Researchers independently categorized the articles in the pilot test based on the best fit among the nine research strategies. After all articles in the pilot test were categorized, the researchers compared their analyses. In instances where the independent categorizations did not match, the researchers re-evaluated the article collaboratively by reviewing the research category definitions, discussing the disagreement thoroughly, and collaboratively assigning the article to a single category. This process allowed the researchers to develop a collaborative interpretation of the research category definitions. This pilot test served as a training session for accurately categorizing the articles for this study.

Each research strategy is defined by a specific design approach, and each is also associated with certain tradeoffs that researchers must make when designing a study. These tradeoffs are inherent flaws that limit the conclusions that can be drawn from a particular research strategy. These tradeoffs refer to three aspects of a study that can vary depending on the research strategy employed. These variable aspects include generalizability from the sample to the target population (external validity), precision in measurement and control of behavioral variables (internal and construct validity), and the issue of realism of context (Scandura & Williams, 2000).

Campbell and Cook (1976) stated that a study has generalizability when the study has external validity across times, settings, and individuals. Formal theory/literature reviews and sample surveys have a high degree of generalizability by establishing the relationship between two constructs and illustrating that this relationship has external validity. A research strategy that has low external validity, but high internal validity is a benefit of the laboratory experiment. In the laboratory experiment, where the degree of measurement precision is high, cause and effect relationships may be determined, but these relationships may not be generalizable for other times, settings, and populations. While the formal theory/literature reviews and sample surveys have a high degree of generalizability and the laboratory experiment has a high degree of precision of measurement, these strategies have low degree of contextual realism. The only two strategies that maximize degree of contextual realism are field studies that use either primary or secondary data because the data is collected in an organizational setting (Scandura & Williams, 2000). The other four strategies maximize neither generalizability, nor degree of precision in measurement, nor degree of contextual realism. This point illustrates the futility of using only one strategy when conducting fintech research. Because no single strategy can maximize all types of validity, it is best for researchers to use a variety of research strategies.

Two coders independently reviewed and classified each article according to research strategy. Only a few articles were reviewed at one sitting to minimize coder fatigue and thus protect intercoder reliability (Neuendorf, 2017). Upon completion of the classification process, agreements and disagreements were tabulated. The percent agreement was 87.5% (N = 216). Then, intercoder reliability (κ = 0.874) using Cohen’s Kappa (Cohen, 1960) and Krippendorf’s Alpha (Krippendorff, 2013) for each methodology (α = 0.859) was calculated. Neuendorf (2017) suggests that a Cohen’s kappa greater than 0.800 is considered acceptable. Krippendorff (2013) stated that researchers could use reliability scores greater than 0.800. Therefore, the calculations for intercoder reliability were well within the acceptable ranges. We calculated the reliability measures prior to discussing disagreements as mandated by Weber (1990). If the original reviewers did not agree on how a particular article was coded, a third reviewer arbitrated the discussion of how the disputed article was to be coded. This process resolved the disputes in all cases.

Phase 3: Categorization by fintech research topic

Typically, the process of categorizing research articles by a specific research topic involves an iterative cycle of brainstorming and discussion sessions among the researchers. This iterative process helps to identify common themes within the data pool of articles. Through the collaborative discussions during this process researchers can synthesize a hierarchical structure within the literature of overarching research topics and more granular level subtopics. The outcome is a better understanding of the current state of a particular stream of research. This iterative process was modified for this specific study on the topic of fintech.

This process resulted in four research topics: Enhance, Impact, Innovate, and Research. The Enhance topic was research that investigates how traditional financial products and services are implemented and improved by using fintech. Examples include using fintech to improve the traditional activities of making personal consumer loans (Di Maggio & Yao, 2021; Gerrans et al., 2021), analyzing the creditworthiness of borrowers (Jagtiani & Lemieux, 2019), and enhancing customer experience in traditional wealth management (Kim et al., 2020). The Impact topic analyses fintech’s influence on industries, governments, and economies and includes the impact of technology on banking industry misconduct (Bertsch et al., 2020), fragility of financial institutions that use various technologies (Fung et al., 2020), how various technologies are affecting the insurance industry (Stoeckli et al., 2018), and the new regulatory models necessary from fintech (Jiang et al., 2021). The Innovate topic explores financial products and services that were created by or made possible by the implementation of fintech with financial products and services such as blockchain, initial coin offerings (ICOs), and cryptocurrencies (Zhao et al., 2021), digital tokens (Benedetti & Nikbakht, 2021), peer to peer lending (Fu, Huang, & Singh, 2021), mobile payments (Du, 2018), crowdfunding (Lin & Pursiainen, 2021), and the analysis of the new business models created by innovations in fintech (Gomber, Kauffman, Parker, & Weber, 2018). The Research topic illustrates the importance and impact of fintech on individuals and society up to and including research on fintech itself. Research that represents this topic include financial literacy (Philippas & Avdoulas, 2020), financial inclusion (Hua & Huang, 2021; Kanga et al., 2021; Senyo, Osabutey, & Kan, 2021), the use of fintech as a research tool (Bradbury et al., 2019), and research on the concept of fintech itself (Bollaert et al., 2021). The authors used these four topics to successfully categorize all 216 articles in the research sample.

To guard against the threats to reliability (Neuendorf, 2017), we once again performed a pilot test on articles not included in the final data pool for this study. Following the adoption of the four research topics, this second pilot study was used as a training session for categorizing articles by research topic. Researchers independently categorized the articles in the pilot test based on the best fit among the four research topics. After all articles in the pilot test were categorized, the researchers compared their analyses. In instances where the independent categorizations did not match, the researchers re-evaluated the article collaboratively by reviewing the research category definitions, discussing the disagreement thoroughly, and collaboratively assigning the article to a single category. This process allowed the researchers to develop a collaborative interpretation of the research topic definitions (see Table 9). Once we established the topic definitions, we independently placed each article in one fintech category. As before, we categorized only a few articles at a time to minimize coder fatigue and thus protect intercoder reliability (Neuendorf, 2017).

Table 9 Fintech research topics

Upon completion of the classification process, agreements and disagreements were tabulated. The percent agreement was 86.1% (N = 216). Then, intercoder reliability (κ = 0.860) using Cohen’s Kappa (Cohen, 1960) and Krippendorf’s Alpha (Krippendorff, 2013) for each methodology (α = 0.815) was calculated. Neuendorf (2017) suggests that a Cohen’s kappa greater than 0.800 is considered acceptable. Krippendorff (2013) stated that researchers could use reliability scores greater than 0.800. Therefore, the calculations for intercoder reliability were well within the acceptable ranges. We calculated the reliability measures prior to discussing disagreements as mandated by Weber (1990). If the original reviewers did not agree on how a particular article was coded, a third reviewer arbitrated the discussion of how the disputed article was to be coded. This process resolved the disputes in all cases.

Results

To identify gaps and needs in the research (Webster & Watson, 2002), we hope to paint a representative landscape of the current fintech literature base. To examine the current state of this research, the authors conducted a literature review and analysis in three phases. Phase 1 accumulated a representative pool of fintech articles, and the articles were then analyzed with respect to year of publication, journal, and author. Phase 2 briefly discussed the research strategies set forth by Scandura and Williams (2000) and the results of the classification of the articles by those research strategies. Phase 3 involved the creation and use of four fintech topics, a short discussion of each topic, and the results of the classification of each article within the research topics. These results are discussed in the following paragraphs.

Results of Phase 1

Using the described search criteria within the selected journals, we collected a total of 216 articles. For the complete list of Finance articles in our sample (N = 146), see Appendix 1. For the complete list of Information Systems articles in our sample (N = 70), see Appendix 2. In phase 1, we further analyzed the articles’ year of publication, journal, and author. Figure 3 shows the number of articles per year in our sample. Although no articles were collected prior to 2016, there is a dramatically increasing trend over the 6-year period of 2016 through 2021. From 2020 to 2021, the number of articles more than doubled, with N = 52 in 2020 and N = 105 in 2021. With fintech issues becoming ever more important to researchers and practitioners, this drastic increase comes as no surprise.

Fig. 3

Number of fintech articles published per year

We analyzed the productivity of authors who published in this line of research by assigning scores based on each author’s share of each article. For projects with multiple authors, each co-author was given an equal share of the credit. An author who published an article alone was assigned a score of 1.0. For a two-author article, each author earned a score of 0.500, three authors shared 0.333, and so on. Authorship order was not calculated into this formula. We totaled the scores for each Finance author, then ranked the authors according to their totaled scores in descending order. The results of the top 43 fintech authors in Finance are displayed in Table 10. This system rewards both quantity of research and ownership of research. The top ranked Finance researcher (Schwienbacher, A.) and the second ranked research (Selim, M.) both had a sole-author paper and co-authorship on another article in the Finance sample. All others who wrote a sole-author research article tied for third place. All of the remaining authors in this list co-authored more than one fintech research article published in Finance, so their scores are above 0.500.

Table 10 Fintech finance authors ranked by score

Similarly, the scores for each Information Systems author were totaled, and the authors were sorted from highest to lowest scores. The results of the top 13 fintech authors in the Information Systems sample are displayed in Table 11. This system rewards both quantity of research and ownership of research. The top ranked researcher (Gozman, D.) had co-authorship on many articles in the Information Systems sample. All the second-ranked authors had a sole author paper (score = 1.0). The remaining authors who had a score greater than 0.500 were also ranked in the sample. A score greater than 0.500 indicates having more than one co-authorship in the sample.

Table 11 Fintech information systems authors ranked by score

Results of Phase 2

The results of the categorization of the 216 articles according to the nine research strategies described by Scandura and Williams (2000) are summarized in Table 12. Of the 216 articles, 104 articles (48.15%) were classified as Field Study—Secondary Data making this category the most used research strategy. With 62 articles (28.70%), Formal Theory/Literature Review was the second most prevalent research strategy. Following were Sample Survey with 23 articles (10.65%) and Field Study—Primary Data with 20 articles (9.26%). The remaining categories had three or fewer articles. These top four research strategies composed of 96.76% of the articles in the sample. No articles were classified as a Judgment Task nor a Field Experiment. These four strategies are exploratory in nature and indicate the beginnings of a body of research (Scandura & Williams, 2000). Further categorization and analysis of the articles with respect to fintech topic categories were conducted in the third phase of this research project.

Table 12 Articles per fintech research strategy

Results of Phase 3

Table 13 shows the number of articles per fintech research topic category. These four categories provided a topic area classification for all the 216 articles in our research sample. Of the 216 articles, 38.43% were classified as “Research,” making it the most prevalent fintech topic category. This result is not surprising because the content analyzed was collected from research publishing outlets. Closely following were “Impact” and “Innovate” (21.76%) tying for second place. “Enhance” was the least popular with 18.06% of the articles. These four research strategies accounted for 100% of the articles in the sample. This illustration of the share of fintech research that is represented by each topic reveals the amount of attention fintech is receiving in Finance journals across a new, yet diverse, research stream.

Table 13 Articles per fintech research topic

Fintech research strategies versus topics

By plotting fintech research topics against research strategies (Table 14), many of the gaps in fintech research are exposed. In our minds, these gaps exist for two reasons. First, some of these research strategies are not prevalent in Finance and IS research. Because some top research journals do not accept papers that use non-traditional or qualitative research strategies, researchers tend to avoid unorthodox strategies. Second, some of these categories have not been studied because they represent a relatively new phenomenon, of which the research has not caught up with the business reality. The great news for researchers interested in fintech is that this domain should provide research opportunities for years to come.

Table 14 Fintech research strategies vs. topics

Almost half (48.15%) of the journal articles in this study use the Field Study—Secondary Data research strategy across all research topics. Therefore, classifying the sources of the secondary data used in this research may be valuable for new researchers by providing them insights and sources for future research. The use of Formal Theory/Literature Review (28.7%) and Sample Survey (10.65%) research strategies indicates the exploratory nature of the current state of fintech research. We speculate four reasons for the top three strategies used to study fintech to be prevalent and appropriate for the early stages of research. First, secondary data is common in Finance research with the common practice of using freely available data from financial markets. This abundance of financial data is augmented by the availability of premium financial information services as a source of data for research projects. Second, in these exploratory years of fintech research, formal theory/literature reviews are appropriate to determine what other strategies are being used in the research and to find reference disciplines that are conducting related research. Third, researchers in business schools tend to be more skilled in administering literature reviews, field studies (with primary and secondary data), and sample surveys than in the strategies of laboratory experiment, field experiments, experimental simulation, judgment task, and computer simulation. Finally, organizations are less likely to commit to certain strategies (e.g., primary field studies and field experiments) because these strategies are more expensive for the organizations. These types of research strategies are very labor intensive to the organization being studied because they require records to be examined, personnel to be interviewed, and senior managers to devote large amounts of their expensive time to help facilitate the research project.

Contributions

To date, no study has examined fintech research topics in words, content, or methodologies. The purpose of this study is to synthesize the methodologies and content of all fintech article from the past 20 year encompassing all journals on the Australian Business Deans list that have a rating of A*, A, and B. This study finds that the majority of fintech research has been conducted over the past 4 years, with the number of articles significantly increasing during that period. The majority of this research is focused on banking, credit, lending, and intermediaries. However, many other subjects are yet to be covered in a robust manner. Despite the proliferation of fintech research, there unfortunately is no standard set of best practices or methodological norms that researchers can use as of yet. Our findings show that fintech research is in its infancy.

Limitations

The current analysis of the fintech literature in this study has limitations and should be enhanced with future research efforts. Future literature reviews could expand article searches to full article text searches, search a broader domain of research outlets (such as adding the C journals in the ABDC journal list), and include other fintech related search terms. Our literature analysis is meant to serve as a representative sample of articles and not a comprehensive and exhaustive analysis of the entire population of articles published on the topic of fintech.

This study provided a content analysis of the current state of the research with respect to research strategy and topic at the journals on the ABDC list. Other publication outlets may be publishing greater quantities of fintech research with similar quality as the journals in our sample.

Directions for future research

For researchers to continue to attempt to answer the important questions in fintech, future studies need to employ a wider variety of research strategies to investigate these important issues. Scandura and Williams (2000) stated that looking at research strategies employed over time by triangulation in each subject area can provide useful insights into how theories are developing. In addition to the lack of variety in research strategy, very little triangulation has occurred during the timeframe used to conduct this literature review. This absence of coordinated theory development causes the research in fintech to appear haphazard and unfocused. Clearly, future studies should consider the identified gaps and consider the future research role relative to generalizability, precision of measure, and realism of context.

Future efforts should also consider the four research topics with respect to the research strategies. To further investigate this body of research, future studies could explore the fintech topics in depth by creating subtopics within the four topics in the study. For example, fintech will be deployed by organizations to improve their current business processes for future study under the Enhance topic. For the Impact, many of these fintech modalities have not been in place long enough for researchers, practitioners, governments, and other stakeholders to collect analyze data on how industries, governments, and economies are affected on a short or long-time horizon. As previously unknown business models and technologies combine fintech and artificial intelligence, new opportunities for research will be presented for researchers and practitioners alike to explore Innovate topic. As the number and quality of research grows in Enhance, Impact, and Innovate grows, this will give researchers in disciplines as varied as Economics, Engineering, Psychology, Sociology, and others to contribute to the research body of fintech and how this concept is progressing across time and a variety of research streams.

Future studies could take a more in-depth look at the various business models or fintech strategies associated with this research stream. Moreover, much of the research in our sample reports the new technologies and issues in fintech without attempting to explain the fundamental issues of the technology implemented or the effects of these technologies on individuals, organizations, and society. This is to be expected in the exploratory stages of research in a subject area.

Conclusion

This study used the content analysis methodology to create a current, cross-disciplinary image of the current state of fintech research in the top Finance and Information Systems journals across time, research strategy, and topic to classify this concept of financial technologies. Further, this study illustrates the future potential of fintech domain across both research strategy and topic. Despite the efforts of the researchers in the article sample, fintech is in the beginning stages of the research stream. The bad news is that much research needs to take place in this domain using a variety of research strategies over time to develop best practices for practitioners and theory for the research domain. In this sample, most of the research had been published in the previous four years, and the good news for researchers and practitioners alike is that many of the topics and research strategies in this research are open for future research efforts including some research strategy and topic areas that are completely unresearched (Table 14). As more practitioners deploy more fintech modalities, researchers will have the opportunity to create even more novel and rigorous research studies. We hope that this content analysis has laid the foundation for such efforts that will enhance the body of knowledge and theoretical progression relative to fintech.

Appendices

Appendix 1 Sample of 146 fintech finance articles

Agarwal, S., & Zhang, J. (2020). Fintech, lending and payment innovation: A review. Asia-Pacific Journal of Financial Studies, 49(3), 353-367. https://doi.org/10.1111/ajfs.12294

Ahmed, S., Grobys, K., & Sapkota, N. (2020). Profitability of technical trading rules among cryptocurrencies with privacy function. Finance Research Letters, 35. https://doi.org/10.1016/j.frl.2020.101495

Akyildirim, E., Corbet, S., Sensoy, A., & Yarovaya, L. (2020). The impact of blockchain related name changes on corporate performance. Journal of Corporate Finance, 65. https://doi.org/10.1016/j.jcorpfin.2020.101759

Akyildirim, E., Sensoy, A., Gulay, G., Corbet, S., & Salari, H. N. (2021). Big data analytics, order imbalance and the predictability of stock returns. Journal of Multinational Financial Management, 62. https://doi.org/10.1016/j.mulfin.2021.100717

Albarrak, M. S., & Alokley, S. A. (2021). Fintech: Ecosystem, opportunities and challenges in Saudi Arabia. Journal of Risk and Financial Management, 14(10). https://doi.org/10.3390/jrfm14100460

Altamura, C. E., & Daunton, M. (2020). Finance, financiers and financial centres: a special issue in honour of Youssef Cassis Introduction. Financial History Review, 27(3), 283-302. https://doi.org/10.1017/s0968565020000153

Altwijry, O. I., Mohammed, M. O., Hassan, M. K., & Selim, M. Developing a Shari'ah based fintech money creation free SFMCF model for Islamic banking. International Journal of Islamic and Middle Eastern Finance and Management. https://doi.org/10.1108/imefm-05-2021-0189

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Sybirianska, Y., Dyba, M., Britchenko, I., Ivashchenko, A., Vasylyshen, Y., & Polishchuk, Y. (2018). Fintech platforms in sme’s financing: eu experience and ways of their application in Ukraine. Investment Management and Financial Innovations, 15(3), 83-96. https://doi.org/10.21511/imfi.15(3).2018.07

Takeda, F., Takeda, K., Takemura, T., & Ueda, R. (2021). The impact of information technology investment announcements on the market value of the Japanese regional banks. Finance Research Letters, 41. https://doi.org/10.1016/j.frl.2020.101811

Tantri, P. (2021). Fintech for the poor: Financial intermediation without discrimination. Review of Finance, 25(2), 561-593. https://doi.org/10.1093/rof/rfaa039

Thakor, A. V. (2020). Fintech and banking: What do we know? Journal of Financial Intermediation, 41. https://doi.org/10.1016/j.jfi.2019.100833

Tseng, P. L., & Guo, W. C. (2021). Fintech, credit market competition, and bank asset quality. Journal of Financial Services Research. https://doi.org/10.1007/s10693-021-00363-y

Uddin, A., & Yu, D. T. (2020). Latent factor model for asset pricing. Journal of Behavioral and Experimental Finance, 27. https://doi.org/10.1016/j.jbef.2020.100353

Uddin, M. H., Mollah, S., & Ali, M. H. (2020). Does cyber tech spending matter for bank stability? International Review of Financial Analysis, 72. https://doi.org/10.1016/j.irfa.2020.101587

Ullah, A., Pinglu, C., Ullah, S., Qian, N. Y., & Zaman, M. (2021). Impact of intellectual capital efficiency on financial stability in banks: Insights from an emerging economy abstract. International Journal of Finance & Economics. https://doi.org/10.1002/ijfe.2512

Vasenska, I., Dimitrov, P., Koyundzhiyska-Davidkova, B., Krastev, V., Durana, P., & Poulaki, I. (2021). Financial transactions using Fintech during the Covid-19 crisis in Bulgaria. Risks, 9(3). https://doi.org/10.3390/risks9030048

Wang, R., Liu, J. T., & Luo, H. (2021). Fintech development and bank risk taking in China. European Journal of Finance, 27(4-5), 397-418. https://doi.org/10.1080/1351847x.2020.1805782

Wang, Y., & Drabek, Z. (2021). Adverse selection in P2P lending: Does peer screening work efficiently?-Empirical evidence from a P2P platform. International Journal of Financial Studies, 9(4). https://doi.org/10.3390/ijfs9040073

Yang, D., & Li, M. (2018). Evolutionary approaches and the construction of technology-driven regulations. Emerging Markets Finance and Trade, 54(14), 3256-3271. https://doi.org/10.1080/1540496x.2018.1496422

Yang, W., Sui, X. P., & Qi, Z. (2021). Can fintech improve the efficiency of commercial banks?-An analysis based on big data. Research in International Business and Finance, 55. https://doi.org/10.1016/j.ribaf.2020.101338

Yao, T., & Song, L. R. (2021). Fintech and the economic capital of Chinese commercial bank's risk: Based on theory and evidence. International Journal of Finance & Economics. https://doi.org/10.1002/ijfe.2528

Yao, Y. H., Li, J. P., & Sun, X. L. (2021). Measuring the risk of Chinese Fintech industry: Evidence from the stock index. Finance Research Letters, 39. https://doi.org/10.1016/j.frl.2020.101564

Yehorycheva, S., Fysun, I., Hudz, T., Palchuk, O., & Boiko, N. (2020). Innovations in the insurance market of a developing country: Case of Ukraine. Investment Management and Financial Innovations, 17(4), 175-188. https://doi.org/10.21511/imfi.17(4).2020.17

Zhang, A. L., Wang, S. Y., Liu, B., & Liu, P. (2020). How fintech impacts pre- and post-loan risk in Chinese commercial banks. International Journal of Finance & Economics. https://doi.org/10.1002/ijfe.2284

Zhang, X., & Wu, C. (2018). Continuous cash flow payment: Theories and practice framework. Emerging Markets Finance and Trade, 54(4), 774-782. https://doi.org/10.1080/1540496x.2016.1241706

Zhao, X. J., Hou, W. X., An, J. F., Liu, X. D., & Zhang, Y. (2021). Initial coin offerings: What rights do investors have? European Journal of Finance, 27(4-5), 305-320. https://doi.org/10.1080/1351847x.2020.1858130

Zhong, W. Q., & Jiang, T. F. (2021). Can internet finance alleviate the exclusiveness of traditional finance? Evidence from Chinese P2P lending markets. Finance Research Letters, 40. https://doi.org/10.1016/j.frl.2020.101731

Zhou, X., & Chen, S. (2021). Fintech innovation regulation based on reputation theory with the participation of new media. Pacific-Basin Finance Journal, 67. https://doi.org/10.1016/j.pacfin.2021.101565

Appendix 2 Sample of 70 Fintech IS articles

Alam, M. M., Awawdeh, A. E., & Bin Muhamad, A. I. (2021). Using e-wallet for business process development: Challenges and prospects in Malaysia. Business Process Management Journal, 27(4), 1142-1162. https://doi.org/10.1108/bpmj-11-2020-0528

Alhassan, M. D., Kolog, E. A., & Boateng, R. (2020). Effect of gratification on user attitude and continuance use of mobile payment services: A developing country context. Journal of Systems and Information Technology, 22(4), 353-380. https://doi.org/10.1108/JSIT-01-2020-0010

Alt, R., Beck, R., & Smits, M. T. (2018). Fintech and the transformation of the financial industry. Electronic Markets, 28(3), 235-243. https://doi.org/10.1007/s12525-018-0310-9

Alyakoob, M., Rahman, M. S., & Wei, Z. Y. (2021). Where you live matters: local bank competition, online marketplace lending, and disparity in borrower benefits. Information Systems Research, 32(4), 1390-1411. https://doi.org/10.1287/isre.2021.1043

Au, C. H., Tan, B. N., & Sun, Y. (2020). Developing a P2P lending platform: Stages, strategies and platform configurations. Internet Research, 30(4), 1229-1249. https://doi.org/10.1108/intr-03-2019-0099

Barbu, C. M., Florea, D. L., Dabija, D. C., & Barbu, M. C. R. (2021). Customer experience in fintech. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1415-1433. https://doi.org/10.3390/jtaer16050080

Belanche, D., Casalo, L. V., & Flavian, C. (2019). Artificial intelligence in fintech: Understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411-1430. https://doi.org/10.1108/imds-08-2018-0368

Bongomin, G. O. C., & Ntayi, J. M. (2020). Mobile money adoption and usage and financial inclusion: Mediating effect of digital consumer protection. Digital Policy Regulation and Governance, 22(3), 157-176. https://doi.org/10.1108/dprg-01-2019-0005

Bunnell, L., Osei-Bryson, K. M., & Yoon, V. Y. (2020). FinPathlight: Framework for an multiagent recommender system designed to increase consumer financial capability. Decision Support Systems, 134. https://doi.org/10.1016/j.dss.2020.113306

Burtch, G., Hong, Y. L., & Liu, D. (2018). The Role of provision points in online crowdfunding. Journal of Management Information Systems, 35(1), 117-144. https://doi.org/10.1080/07421222.2018.1440764

Chatterjee, S., Sarker, S., Lee, M. J., Xiao, X., & Elbanna, A. (2021). A possible conceptualization of the information systems (IS) artifact: A general systems theory perspective(1). Information Systems Journal, 31(4), 550-578. https://doi.org/10.1111/isj.12320

Chen, X. R., Hu, X. J., & Ben, S. L. (2021). How individual investors react to negative events in the fintech era? Evidence from China's peer-to-peer lending market. Journal of Theoretical and Applied Electronic Commerce Research, 16(1), 52-70. https://doi.org/10.4067/s0718-18762021000100105

Coffie, C. P. K., Hongjiang, Z., Mensah, I. A., Kiconco, R., & Simon, A. E. O. (2021). Determinants of fintech payment services diffusion by SMEs in Sub-Saharan Africa: evidence from Ghana. Information Technology for Development, 27(3), 539-560. https://doi.org/10.1080/02681102.2020.1840324

Cui, X., Liu, W., & He, Z. (2021). Attribute reduction algorithm of enterprise credit evaluation and its applications for web-based communities. International Journal of Web Based Communities, 17(4), 293-304. https://doi.org/10.1504/IJWBC.2021.119473

Currie, W. L., Gozman, D. P., & Seddon, J. J. M. (2018). Dialectic tensions in the financial markets: A longitudinal study of pre- and post-crisis regulatory technology. Journal of Information Technology, 33(4), 304-325. https://doi.org/10.1057/s41265-017-0047-5

Dehnert, M., & Schumann, J. (2022). Uncovering the digitalization impact on consumer decision-making for checking accounts in banking. Electronic Markets, 32(3). https://doi.org/10.1007/s12525-022-00524-4

Dhar, V., & Stein, R. M. (2017). Economic and business dimensions fintech platforms and strategy. Communications of the Acm, 60(10), 32-35. https://doi.org/10.1145/3132726

Du, K. (2018). Complacency, capabilities, and institutional pressure: Understanding financial institutions' participation in the nascent mobile payments ecosystem. Electronic Markets, 28(3), 307-319. https://doi.org/10.1007/s12525-017-0267-0

Du, W. Y., Pan, S. L., Leidner, D. E., & Ying, W. C. (2019). Affordances, experimentation and actualization of fintech: A blockchain implementation study. Journal of Strategic Information Systems, 28(1), 50-65. https://doi.org/10.1016/j.jsis.2018.10.002

Fu, R. S., Huang, Y., & Singh, P. V. (2021). Crowds, lending, machine, and bias. Information Systems Research, 32(1), 72-92. https://doi.org/10.1287/isre.2020.0990

Gao, Z., Guo, Z., & Tang, Q. (2021). How do monetary incentives influence giving? An empirical investigation of matching subsidies on kiva. Information Systems and e-Business Management. https://doi.org/10.1007/s10257-021-00515-6

Ge, R. Y., Feng, J., Gu, B., & Zhang, P. Z. (2017). Predicting and deterring default with social media information in peer-to-peer lending. Journal of Management Information Systems, 34(2), 401-424. https://doi.org/10.1080/07421222.2017.1334472

Gimpel, H., Rau, D., & Roglinger, M. (2018). Understanding fintech start-ups - A taxonomy of consumer-oriented service offerings. Electronic Markets, 28(3), 245-264. https://doi.org/10.1007/s12525-017-0275-0

Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220-265. https://doi.org/10.1080/07421222.2018.1440766

Gozman, D., Liebenau, J., & Mangan, J. (2018). The Innovation mechanisms of fintech start-ups: Insights from SWIFT's Innotribe competition. Journal of Management Information Systems, 35(1), 145-179. https://doi.org/10.1080/07421222.2018.1440768

Haitao, S. (2020). Big data analysis of e-commerce loan risk of college students in the context of network finance. Information Systems and e-Business Management, 18(3), 439-454. https://doi.org/10.1007/s10257-019-00424-9

Han, H., Teng, J., Xia, J. R., Wang, Y. H., Guo, Z. H., & Li, D. Q. (2021). Predict high-frequency trading marker via manifold learning. Knowledge-Based Systems, 213. https://doi.org/10.1016/j.knosys.2020.106662

Hendershott, T., Zhang, X. Q., Zhao, J. L., & Zheng, Z. Q. (2021). Fintech as a game changer: Overview of research frontiers. Information Systems Research, 32(1), 1-17. https://doi.org/10.1287/isre2021.0997

Hua, X. P., Huang, Y. P., & Zheng, Y. F. (2019). Current practices, new insights, and emerging trends of financial technologies. Industrial Management & Data Systems, 119(7), 1401-1410. https://doi.org/10.1108/imds-08-2019-0431

Ilk, N., Shang, G. Z., Fan, S. K., & Zhao, J. L. (2021). Stability of transaction fees in BitCoin: a supply and demand perspective. MIS Quarterly, 45(2), 563-592. https://doi.org/10.25300/misq/2021/15718

Jiang, Y., Ho, Y. C., Yan, X. B., & Tan, Y. (2018). Investor platform choice: Herding, platform attributes, and regulations. Journal of Management Information Systems, 35(1), 86-116. https://doi.org/10.1080/07421222.2018.1440770

Jin, Y., Ding, C., Duan, Y., & Cheng, H. K. (2020). Click to success? The temporal effects of facebook likes on crowdfunding. Journal of the Association for Information Systems, 21(5), 1191-1213. https://doi.org/10.17705/1jais.00634

Jinasena, D. N., Spanaki, K., Papadopoulos, T., & Balta, M. E. (2023). Success and failure retrospectives of fintech projects: A case study approach. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-10079-4

Jung, D., Dorner, V., Weinhardt, C., & Pusmaz, H. (2018). Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Markets, 28(3), 367-380. https://doi.org/10.1007/s12525-017-0279-9

Lagna, A., & Ravishankar, M. N. (2022). Making the world a better place with fintech research. Information Systems Journal, 32(1), 61-102. https://doi.org/10.1111/isj.12333

Leong, C., Tan, B., Xiao, X., Tan, F. T. C., & Sun, Y. (2017). Nurturing a fintech ecosystem: The case of a youth microloan startup in China. International Journal of Information Management, 37(2), 92-97. https://doi.org/10.1016/j.ijinfomgt.2016.11.006

Li, M. Y., Qin, Y., Liu, B., & Chu, X. W. (2021). Enhancing the efficiency and scalability of blockchain through probabilistic verification and clustering. Information Processing & Management, 58(5). https://doi.org/10.1016/j.ipm.2021.102650

Lim, S. H., Kim, D. J., Hur, Y., & Park, K. (2019). An empirical study of the impacts of perceived security and knowledge on continuous intention to use mobile fintech payment services. International Journal of Human-Computer Interaction, 35(10), 886-898. https://doi.org/10.1080/10447318.2018.1507132

Mai, F., Shan, Z., Bai, Q., Wang, X., & Chiang, R. H. L. (2018). How does social media impact Bitcoin value? A test of the silent majority hypothesis. Journal of Management Information Systems, 35(1), 19-52. https://doi.org/10.1080/07421222.2018.1440774

Muthukannan, P., Tan, B., Gozman, D., & Johnson, L. (2020). The emergence of a fintech ecosystem: A case study of the Vizag fintech valley in India. Information & Management, 57(8). https://doi.org/10.1016/j.im.2020.103385

Muthukannan, P., Tan, B., Tan, F. T., & Leong, C. (2021). Novel mechanisms of scalability of financial services in an emerging market context: Insights from Indonesian fintech Ecosystem. International Journal of Information Management, 61. https://doi.org/10.1016/j.ijinfomgt.2021.102403

Nayak, B., Bhattacharyya, S. S., & Krishnamoorthy, B. (2019). Integrating wearable technology products and big data analytics in business strategy: A study of health insurance firms. Journal of Systems and Information Technology, 21(2), 255-275. https://doi.org/10.1108/JSIT-08-2018-0109

Odoom, R., & Kosiba, J. P. (2020). Mobile money usage and continuance intention among micro enterprises in an emerging market – The mediating role of agent credibility. Journal of Systems and Information Technology, 22(4), 97-117. https://doi.org/10.1108/JSIT-03-2019-0062

Ozili, P. K. (2020). Contesting digital finance for the poor. Digital Policy Regulation and Governance, 22(2), 135-151. https://doi.org/10.1108/dprg-12-2019-0104

Pousttchi, K., & Dehnert, M. (2018). Exploring the digitalization impact on consumer decision-making in retail banking. Electronic Markets, 28(3), 265-286. https://doi.org/10.1007/s12525-017-0283-0

Puschmann, T. (2017). Fintech. Business & Information Systems Engineering, 59(1), 69-76. https://doi.org/10.1007/s12599-017-0464-6

Ryu, H. S. (2018). What makes users willing or hesitant to use fintech? The moderating effect of user type. Industrial Management & Data Systems, 118(3), 541-569. https://doi.org/10.1108/imds-07-2017-0325

Saura, J. R., Reyes-Menendez, A., deMatos, N., & Correia, M. B. (2021). Identifying startups business opportunities from UGC on Twitter chatting: An exploratory analysis. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 1929-1944. https://doi.org/10.3390/jtaer16060108

Senyo, P. K., Karanasios, S., Gozman, D., & Baba, M. (2022). Fintech ecosystem practices shaping financial inclusion: The case of mobile money in Ghana. European Journal of Information Systems, 31(1), 112-127. https://doi.org/10.1080/0960085x.2021.1978342

Senyo, P. K., Osabutey, E. L. C., & Kan, K. A. S. (2021). Pathways to improving financial inclusion through mobile money: A fuzzy set qualitative comparative analysis. Information Technology & People, 34(7), 1997-2017. https://doi.org/10.1108/itp-06-2020-0418

Shiau, W. L., Yuan, Y., Pu, X. D., Ray, S., & Chen, C. C. (2020). Understanding fintech continuance: Perspectives from self-efficacy and ECT-IS theories. Industrial Management & Data Systems, 120(9), 1659-1689. https://doi.org/10.1108/imds-02-2020-0069

Stewart, H., & Jurjens, J. (2018). Data security and consumer trust in fintech innovation in Germany. Information and Computer Security, 26(1), 109-128. https://doi.org/10.1108/ics-06-2017-0039

Stoeckli, E., Dremel, C., & Uebernickel, F. (2018). Exploring characteristics and transformational capabilities of InsurTech innovations to understand insurance value creation in a digital world. Electronic Markets, 28(3), 287-305. https://doi.org/10.1007/s12525-018-0304-7

Stojanovic, B., Hofer-Schmitz, K., & Kleb, U. (2020). APT datasets and attack modeling for automated detection methods: A review. Computers & Security, 92. https://doi.org/10.1016/j.cose.2020.101734

Tan, T. H., Zhang, Y., Heng, C. S. A., & Ge, C. M. (2021). Empowerment of grassroots consumers: A revelatory case of a Chinese fintech innovation. Journal of the Association for Information Systems, 22(1), 179-203. https://doi.org/10.17705/1jais.00658

Tao, Q. Z., Dong, Y. Z., & Lin, Z. M. (2017). Who can get money? Evidence from the Chinese peer-to-peer lending platform. Information Systems Frontiers, 19(3), 425-441. https://doi.org/10.1007/s10796-017-9751-5

Vo, A., Chapman, T. A., & Lee, Y. S. Examining Bitcoin and economic determinants: an evolutionary perspective. Journal of Computer Information Systems. https://doi.org/10.1080/08874417.2020.1865851

Vucetic, M., Brokesova, Z., Hudec, M., & Pastorakova, E. (2022). Financial literacy and psychological disaster preparedness: Applicability of approach based on fuzzy functional dependencies. Information Processing & Management, 59(2). https://doi.org/10.1016/j.ipm.2021.102848

Wang, Z., & Ben, S. (2021). Effect of consumers’ online shopping on their investment in money market funds on ecommerce platforms. Information Systems and e-Business Management. https://doi.org/10.1007/s10257-021-00516-5

Wang, Z., Jiang, C. Q., & Zhao, H. M. (2021). Know where to invest: Platform risk evaluation in online lending. Information Systems Research. https://doi.org/10.1287/isre.2021.1083

Wang, Z. N., Guan, Z. Z., Hou, F. F., Li, B. Y., & Zhou, W. Y. (2019). What determines customers' continuance intention of fintech? Evidence from YuEbao. Industrial Management & Data Systems, 119(8), 1625-1637. https://doi.org/10.1108/imds-01-2019-0011

Wessel, M., Gleasure, R., & Kauffman, R. J. (2021). Sustainability of rewards-based crowdfunding: a quasi-experimental analysis of funding targets and backer satisfaction. Journal of Management Information Systems, 38(3), 612-646. https://doi.org/10.1080/07421222.2021.1987622

Wingreen, S. C., Kavanagh, D., Ennis, P. J., & Miscione, G. (2020). Sources of cryptocurrency value systems: the case of Bitcoin. International Journal of Electronic Commerce, 24(4), 474-496. https://doi.org/10.1080/10864415.2020.1806469

Wu, M. E., Syu, J. H., Srivastava, G., & Lin, J. C. W. (2022). Informative index for investment based on Kelly criterion. Enterprise Information Systems. https://doi.org/10.1080/17517575.2021.1939425

Xie, J. L., Ye, L. Y., Huang, W., & Ye, M. (2021). Understanding fintech platform adoption: Impacts of perceived value and perceived risk. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1893-1911. https://doi.org/10.3390/jtaer16050106

Xie, P., Chen, H. L., & Hu, Y. J. (2020). Signal or noise in social media discussions: the role of network cohesion in predicting the Bitcoin market. Journal of Management Information Systems, 37(4), 933-956. https://doi.org/10.1080/07421222.2020.1831762

Xu, Y. L., Bao, H. J., Zhang, W. Y., & Zhang, S. (2021). Which financial earmarking policy is more effective in promoting fintech innovation and regulation? Industrial Management & Data Systems, 121(10), 2181-2206. https://doi.org/10.1108/imds-11-2020-0656

Yeh, J. Y., & Chen, C. H. (2022). A machine learning approach to predict the success of crowdfunding fintech project. Journal of Enterprise Information Management. https://doi.org/10.1108/jeim-01-2019-0017

Yun, J. J., Liu, Z., & Zhao, X. F. (2021). Introduction: Ambidextrous open innovation in the 4th industrial revolution. Science Technology and Society, 26(2), 183-200. https://doi.org/10.1177/09717218211006969

Zhao, Y., & Chen, X. H. (2022). The relationship between the withdrawal of the digital economy's innovators, government interventions, the marketization level and market size based on big data. Journal of Enterprise Information Management. https://doi.org/10.1108/jeim-01-2021-0050