From Innovation to Use: Configurational Pathways to High Fintech Use Across User Groups

Abstract

Despite high expectations for Fintech growth, its real-world expansion has fallen short due to its inherent complexity. Although Fintech is innovative, its multidimensional nature has made it difficult for companies to develop effective, tailored solutions for its diverse user groups. To foster the development of effective and practical Fintech solutions that can expand the user base, a novel and integrative approach is required. Therefore, this study aims to explore specific solutions to enhance Fintech use by holistically combining and intertwining various attributes. Based on the diffusion of innovation theory and the information systems success model, we propose a conceptual Fintech model consisting of three dimensions: innovation, financial service, and information technology. To investigate this model, we adopt fuzzy-set qualitative comparative analysis (fsQCA), a set-theoretic method suited to identifying combinations of Fintech attributes that lead to specific outcomes. The results reveal that the configurations of Fintech attributes leading to high Fintech use differ across four user groups: Infrequent users, Lurkers, Task-driven users, and Power users. The findings also show that information technology plays multifaceted roles depending on its combination with other Fintech attributes. This study explains the interdependencies among Fintech attributes and their combined effects on Fintech use, offering deeper insights into Fintech research through a configurational lens.

1. Introduction

Fintech is revolutionizing the financial industry by integrating finance with information technology (IT), offering customers affordable, quick, high-quality, and diverse financial services [1,2]. South Korea’s Fintech sector is a rapidly expanding market characterized by a highly digitally literate and engaged population. The Korean government has actively promoted the industry by designating it as a key growth area and implementing policies to foster Fintech innovation. This has led to the emergence of major players such as Kakao Pay, Toss, and Naver Pay, which have become “super-apps” integrate various financial services. According to an IMARC Group report (https://www.imarcgroup.com/fintech-market (accessed on 27 March 2025)), the South Korea Fintech market is expected to record a compound annual growth rate (CAGR) of 7.3% from 2025 to 2033. The global Fintech market is also expected to show a CAGR of 15.82% during this period, growing from USD 218.8 billion in 2024 to USD 828.4 billion in 2033. Fintech has now become mainstream in both the financial industry and everyday life.

Although Fintech is fundamentally innovative and delivers significant efficiency, it is an inherently complex phenomenon. This complexity arises from its deliberate combination of innovative financial services and IT, giving it multidimensional characteristics that are not easily understood. Previous research on Fintech adoption and use has examined various factors, including technology acceptance [3,4,5,6,7], user innovativeness [5,6,8], trust and risk-related factors [3,4,7,9] and economic conditions [1,2,10]. However, most studies have primarily examined individual antecedents of Fintech use, providing a limited understanding of Fintech use as a complex, emergent phenomenon [11,12]. While many studies have highlighted the complexity of Fintech, a few have examined it in an interrelated way with an organized theoretical basis. Given the nature of Fintech, a new approach to understanding it is required.

In addition, although the connection between financial services and IT is not new, the expanding and increasingly central role of IT is a defining characteristic of Fintech [10]. Arner, et al. [13] indicated that IT is a core component in Fintech and Ernst and Young [14] emphasized that in this context, IT acts not as a facilitator or enabler for effectively delivering financial services, but as a true innovator that disrupts the existing value chains. The role of IT in Fintech is pivotal, spanning from back-end processes to front-end services such as payments, cross-border transfers, retail banking, lending, and cryptocurrencies. Given the critical role of IT in Fintech business success, customers may perceive the quality of IT as representative of the overall Fintech service quality. Although many studies acknowledge the importance of IT in Fintech, its role remains unexplored.

To bridge these research gaps, we adopt a holistic approach to investigate Fintech use. We propose that Fintech use is driven not by individual attributes within each Fintech dimension, but by the synergistic effects of attributes across multiple dimensions. To test this proposition, this study first identifies three Fintech dimensions from the existing literature and then develops a theoretical framework. Using this framework, we explore the specific attributes that influence Fintech adoption and use, including IT, emphasizing not individual attributes, but the configurations in which they co-occur. In other words, specific combinations of attributes across the dimensions may lead to high Fintech use. We then identify multiple alternative patterns depending on specific user-related factors and propose multiple viable propositions from them. Consequently, our study aims to identify viable patterns of Fintech attributes for achieving high levels of use across different user types, providing valuable insights into the interrelated nature of the attributes within the three Fintech dimensions.

We used empirical data from 254 Fintech users with over three months of experience in South Korea to explore the diverse configurations of Fintech attributes that drive high Fintech use. A fuzzy-set qualitative comparative analysis (fsQCA) was employed to identify specific combinations of interrelated Fintech attributes for achieving high Fintech use. fsQCA has been applied in various academic fields, including sociology, political science, innovation, and information systems (ISs), to provide a deeper understanding of complex phenomena. This study explains the interdependencies among Fintech attributes and their combined effects on Fintech use by adopting a holistic perspective, thereby providing a deeper understanding of Fintech research. Furthermore, we overcome the limitations of traditional statistical methods in the Fintech context by using fsQCA. Our findings highlight the importance of recognizing the multidimensional and interconnected nature of Fintech and suggest effective strategies for practitioners to promote high levels of Fintech use among specific user groups. Such strategies can attract additional users to the Fintech platform, thereby strengthening network effects and enhancing service reliability. In turn, a larger user base can help establish a more stable long-term revenue structure, ultimately supporting the sustainability of Fintech companies [15,16,17].

The remainder of this paper is organized as follows. Section 2 outlines the conceptual model of Fintech and the theories applied. Section 3 describes the attributes of the three Fintech dimensions and proposes the research model. Section 4 details the research methodology using fsQCA, and Section 5 presents the analysis and results. Section 6 discusses the results and their theoretical and practical implications.

2. Theoretical Background

2.1. Three Dimensions of Fintech

Fintech has become a key strategy for delivering superior financial services [13], with new players, such as Fintech companies, offering their services directly to users and bypassing intermediaries. This shift marks a transition from institution-oriented to user-oriented financial services. Traditional financial institutions have focused on risk minimization, often resulting in user inconvenience, while Fintech enhances the efficiency of financial transactions by bypassing traditional channels, providing greater convenience and customization for users [10,14,18]. Fintech promises lower costs, higher efficiency, and greater product personalization for users, transforming into a disruptive innovation that challenges existing financial services. Furthermore, Fintech presents significant opportunities for the existing financial market to overcome growth limits by positively influencing consumer perceptions, which could enhance the adoption and use of financial services and ultimately increase the overall volume of the financial market.

To improve understanding of Fintech innovation, we needed to identify its dimensions, considering that Fintech is a converged service encompassing multiple dimensions. To identify these dimensions more concretely, we first reviewed the literature on Fintech definitions. Ref. [13] describes Fintech as a technology-enabled financial solution, emphasizing the role of IT in delivering these services. Kuo Chuen and Teo [2] define it as innovative financial services or products delivered through new technology. Ryu and Ko [19] clarify it as “innovative and disruptive financial services, where IT is the key factor”. Schueffel [20] proposes it as “a new financial industry that applies technology to improve financial activities” after reviewing more than 200 articles relevant to Fintech. Bureshaid, et al. [21] argue that Fintech has the potential to revolutionize the financial sector.

These definitions consistently highlight terms such as “new” or “innovation”, indicating how Fintech is perceived by its users. As newness is an attribute of innovation, we define innovation as a key dimension of Fintech, encompassing both novelty and innovation. Additionally, all definitions refer to some variation of finance (financial industry or sector, financial activity, or financial services). Because our focus is to understand Fintech from the user perspective rather than from the institutional perspective, we adopt financial services as a dimension in this study. Moreover, technology, particularly IT, is emphasized in most definitions; therefore, we also incorporate IT as a dimension. Based on these definitions, we identify three core dimensions of Fintech: innovation, financial services, and IT. Understanding Fintech use requires exploring those key dimensions. Therefore, we define Fintech as an innovative financial service driven by IT. Based on this definition, we identify the attributes within each dimension that influence Fintech use.

2.2. Diffusion of Innovation Theory and Information Systems Success Model

As mentioned, we developed three dimensions of Fintech to define the Fintech concept. Because many possible attributes can be associated with each dimension, we first needed to establish boundaries and define the scope by creating a theoretical framework. We began this process by reviewing the literature on innovation adoption to identify the specific characteristics that motivate individuals to adopt and utilize Fintech solutions. Using this theoretical foundation, we selected the most significant attributes from each of the three identified Fintech dimensions.

Because Fintech offers innovation to users, we employ the diffusion of innovation (DOI) theory as the first dimension of our theoretical framework. The DOI theory is central to the innovation adoption literature, explaining what initiates and accelerates the use of innovations among individuals [22]. Because this theory provides a well-established understanding of how individuals adopt innovations, it is particularly suited for exploring the innovation dimension of Fintech [23,24,25,26].

Considering Fintech provides innovative financial services, we expand the DOI theory for the second dimension to explain the characteristics of financial services [26,27,28]. As innovations in Fintech may involve new service providers and transactions enabled by IT [29], we focus on innovation literature related to service adoption and use to identify the attributes that create value in the second dimension of Fintech.

For the third dimension, we use the information system success (ISS) model to identify IT characteristics in Fintech. Many technological innovations have emerged through technological development, and their adoption forms a substantial part of the related literature. High-quality IT is essential for Fintech to derive value by creating new business models, providing innovative financial services, and offering new service delivery channels with low transaction costs. Given the critical role of IT in the success of Fintech businesses, users might perceive IT quality as synonymous with overall Fintech quality, suggesting that IT quality is key to influencing Fintech use. Therefore, the ISS model, which focuses on IT quality, is a valuable tool for our framework.

2.3. Configurational Theory

Considering that Fintech is a multidimensional, convergence service, Fintech use behavior may result from different combinations of attributes within the three Fintech dimensions, and specific patterns among them may lead to high Fintech use. Previous IS research has largely viewed the adoption and use of Fintech as a result of numerous isolated variables, failing to fully address complex phenomena such as Fintech use. Therefore, a new approach is required to understand innovative Fintech phenomena. El Sawy, et al. [30] pointed out that, to effectively analyze service innovation with multidimensional characteristics, it is more effective to view it as a complex combination of various conditions from a holistic perspective. Park, et al. [31] suggested that a configurational approach using fsQCA is valuable for social science research topics where concepts are not entirely clear.

Configurational theory posits that patterns of conditions, rather than individual conditions, are stronger determinants of outcomes, and that different combinations of multiple conditions can lead to the same outcome. Although variance theory approaches have been dominant in IS research, configurational theory is considered highly suited for studying Fintech phenomena [30,32]. For example, within the Fintech conceptual model, we employed the two complementary theories. The DOI framework highlights the innovation-related attributes that influence individual adoption, while the ISS model emphasizes the system- and information-related qualities that affect user satisfaction and continuance. Integrating these perspectives is necessary to capture both innovation-driven and system-driven factors, thereby providing a more holistic understanding of Fintech adoption and use. fsQCA from the configurational approach is suitable to further strengthen this integration by examining how these attributes operate in combination rather than in isolation. This can reveal multiple, equifinal pathways leading to high Fintech use.

In line with this approach, we employed the fsQCA method, a set-theoretic tool that systematically examines how key elements combine into configurations. This method enables us to elaborate on, build, and test configurational theories [30,33,34,35,36]. fsQCA offers unique advantages for describing complex relationships among multiple elements, owing to its use of set theory, Boolean algebra, and counterfactual analysis. Unlike traditional methods that identify the net effects of individual independent variables on an outcome, fsQCA focuses on identifying the causal combinations, or “recipes” [37], associated with the outcome—in this case, illustrating how innovation, financial service, and IT combine to produce the outcome of interest (i.e., high Fintech use). fsQCA can manage complex multi-way interactions, in which all elements theoretically relevant to the outcome participate, thereby reducing concerns about unobserved heterogeneity [38]. fsQCA has traditionally been used in sociology and political science for theories and policy derivation through comparisons and has recently gained interest as a methodology in the IS literature [32,36,39,40,41]. Therefore, the configurational approach using fsQCA is well-suited for accurately reflecting and analyzing the complex phenomena of Fintech adoption and use, which are challenging to measure.

3. Research Model of Fintech

3.1. Causal Condition 1: Three Attributes of Innovation

DOI theory defines innovation adoption attributes as the reasons for users to decide to adopt an innovation, forming the basis for the diffusion of innovations among users. Many studies on innovation adoption have relied on Rogers’ [22] DOI theory and further developed it. The DOI theory identifies key innovation attributes that influence adoption, including relative advantage, compatibility, complexity, trialability, observability, risk, and uncertainty. In this study, we focus on the attributes that specifically affect Fintech use within the innovation dimension. There is no consensus on which innovation attributes best explain Fintech use. Many studies have found that only a subset of these attributes explains the specific characteristics of Fintech innovation. In this study, we excluded complexity, observability, and trialability from these six attributes because they do not fit well in the Fintech context. Specifically, many studies have found that complexity does not significantly affect Fintech adoption or use [3,18]. Therefore, measuring complexity may not provide meaningful insights into Fintech context. Observability was omitted because Fintech often involves highly personalized, sensitive financial information, making it difficult to observe. Similarly, trialability was excluded because financial information is sensitive and personal, and Fintech cannot be offered on a trial basis. Although trialability typically helps decrease uncertainty and risk in service adoption, it does not address the uncertainty and risk associated with financial consequences in the context of Fintech. Finally, relative advantage, compatibility, and perceived risk were adopted as attributes of the innovation dimension.

First, relative advantage refers to the perceived superiority of new offerings over alternatives in terms of quality and function [42]. Relative advantage is an internal factor for adopters, demonstrating that user perceptions of service characteristics are pivotal for Fintech adoption [43]. Because Fintech innovation is designed to replace traditional financial services, relative advantage becomes a critical attribute motivating Fintech user. Therefore, we included relative advantage in the set of innovation attributes.

Second, compatibility is another attribute that is well-suited to Fintech. Compatibility is an internal factor for adopters at both individual and organizational levels, reflecting past experiences, beliefs, and motivations [43,44]. Compatibility is generally defined as the degree to which an innovation is perceived to be consistent with existing products or services, past experiences, lifestyles and the needs of potential adopters [22,45]. For this study, we narrowed the scope to Fintech by adopting the concept of meaningfulness, which captures the essence of compatibility but with a better focus on service use. Meaningfulness describes the degree to which a new service appears valuable to users and is able to satisfy their needs [46].

Third, a high level of uncertainty and the risk of financial loss can certainly cause users to hesitate before using Fintech [47], making these critical attributes of Fintech. In the service-related literature, however, uncertainty is often used interchangeably with risk, as risk encompasses the uncertainty about an outcome [48]. Therefore, in this context, perceived risk is a more suitable attribute than uncertainty. We conceptualized perceived risk as a comprehensive concept that reflects the uncertain functional, social, and financial consequences of adopting and using Fintech. In this study, perceived risk is defined as the perceived degree of uncertainty and the potential negative consequences of using a new service.

Building on these attributes, our study further extends the DOI perspective by linking them to sustainability. Relative advantage demonstrates how Fintech can improve efficiency and inclusivity. Meaningfulness shows how it can align with ESG (Environmental, Social, and Governance) principles and societal values. Reducing perceived risk can foster trust and encourage long-term adoption. Taken together, these constructs show how Fintech adoption pathways influence user acceptance and contribute to the development of sustainable financial ecosystems.

However, we excluded complexity, observability, and trialability from the attributes because they do not fit the Fintech context well. Given the lack of relevance in previous research, measuring the degree of complexity is unlikely to provide meaningful insights in the Fintech context. Observability was also omitted from the analysis because Fintech is highly personalized, dealing with sensitive financial information unique to each individual. Offering trial periods for Fintech services is impractical because it does not effectively reduce the uncertainty or risk of financial consequences.

3.2. Causal Condition 2: Two Attributes of Financial Service

Fintech, an IT-driven business model with unique financial features, operates in environments with high perceived risks, such as system malfunctions and fraud [47]. Because of these risks, users often do not fully trust online financial transactions [49]. Ensuring that users perceive Fintech companies as trustworthy is crucial for adoption and use [3,19,50,51]. Financial losses and security failures, such as personal data misuse and fraud, highlight the need for robust security and safety measures in Fintech companies [47]. Therefore, Fintech providers must emphasize secure and reliable financial service delivery [52,53]. Governments also mandate that service firms provide safe and reliable financial services to protect and maintain user assets legally and systematically, ensuring that users feel safe and secure in their financial transactions [53,54].

Trust in service providers and the transaction process is vital for adoption and use, especially for complex services [55]. In particular, institutional-based trust and process-based trust are key attributes in the financial services dimension. Thus, we focused on institutional-based trust, represented by structural assurance, and process-based trust, described by trust in transactions, in this study. Structural assurance, representing institutional-based trust, refers to users’ perception of legal and regulatory structures provided by Fintech companies. It ensures compensation for losses and protection of user information, helping build initial trust in new Fintech organizations. With a high level of structural assurance, users feel safe and secure in using Fintech, allowing them to overcome their fears of financial losses and personal information leakage. Therefore, we adopted structural assurance, defined as a user belief that Fintech companies have protective legal or regulatory structures to ensure that their business can be conducted safely and securely.

Trust in transactions, representing process-based trust, focuses on users’ satisfaction with and confidence in Fintech transactions. This type of trust can enhance satisfaction with transactions and reduce perceived risk, thereby boosting Fintech use [56,57,58]. For example, trust in online payment transactions has been shown to increase the use of mobile payments [58]. Hence, in this study, trust in transactions is defined as the user’s belief that Fintech transactions are trustworthy, leading to higher Fintech use. Therefore, structural assurance and trust in transactions are key attributes for fostering high Fintech use.

3.3. Causal Condition 3: Two Attributes of IT

This study explores the IT dimension of Fintech using the ISS model [59], highlighting system and information quality as critical factors influencing Fintech use. System quality refers to factors such as access speed, ease of use, navigation, and the visual appeal of systems [60,61]. Lee and Chung [62] demonstrated that a user’s first impressions are shaped by their experience with the quality of an IT system. When users experience high-quality systems, they are more likely to trust the service associated with it, leading to increased usage and spending [63]. Conversely, poor system quality undermines user trust, causing dissatisfaction with Fintech systems, thereby leading to low Fintech use. Thus, we define system quality as the user’s perception of the overall performance of Fintech systems.

Information quality refers to the relevance, sufficiency, accuracy, and timeliness of the information provided by Fintech companies [64,65]. For example, users expect to make payments for products or services via mobile payments and to receive payment information anytime and anywhere. If this payment information is inaccurate, insufficient, or outdated, users may doubt the overall reliability of the Fintech company providing it [62,66]. Low-quality information increases a user’s search and information-processing costs [67,68]. Given that users are generally unwilling to invest significant effort or time searching for and evaluating financial transaction information, low-quality information can result in reduced Fintech use. Therefore, in this study, information quality is defined as user perceptions of the quality of information provided during Fintech interactions.

3.4. User Factors: Use Period and Use Frequency

Previous studies have found that individual-level factors such as age, education, use period, and use frequency are significant influences on technology use in various IS contexts [69,70,71]. Fintech use also varies based on users’ personal factors, which are often reflected in key demographic variables [70]. Among these factors, use period and use frequency are more critical than others in Fintech use, as they are directly related to how often and for how long Fintech is actually used. In particular, for Fintech, which provides innovative and convenient financial services in everyday life, use period and use frequency are crucial in experiencing the benefits and perceiving the value of the service. The factors determine how frequently users interact with Fintech and how long they have been familiar with it. To develop strategies for promoting Fintech use, it is essential to investigate why users exhibit different patterns of use based on key factors such as use period and use frequency.

The use period refers to the duration during which users have consistently considered and are actually using a service. This period may be related to the effort and performance expectancy, which may influence usage behavior [71]. In the context of Fintech, the use period can be seen as an indicator of user preference. However, use frequency refers to how often an individual uses a service within a specific time frame. In the mobile context, frequent use signals high user favor and engagement with the service [72]. Moreover, users with different use frequencies tend to have varying levels of use skills that can dynamically change their experience with the service [73]. In other words, frequent users are likely to exhibit loyalty to a service. We incorporate the use period and use frequency as key user factors that could influence the specific configurations of innovation attributes needed to drive high Fintech use.

Based on our theoretical framework, we here propose a configurational model in which Fintech use depends on combinations of the various attributes in the three Fintech dimensions (innovation, financial services, and IT). We assume that different configurations of the seven attributes from the three dimensions produce high or not-high Fintech use. Depending on user factors, the configurations leading to high Fintech use differ. Figure 1 depicts our research model.

4. Research Methodology

4.1. Sample and Data Collection

Following a pre-test, the survey questionnaires were distributed to 1000 participants over three weeks in April 2017 in South Korea. The survey specifically targeted individuals who had actively used one or more Fintech services for more than three months. An initial screening question was included to ensure that respondents fully understood the survey context and to verify their status as current Fintech users. Of the 1000 surveys distributed, 293 responses were collected, and 254 were deemed useful for this study, resulting in a response rate of 25.4%.

To assess the potential impact of non-response bias, we conducted an early–late respondent analysis following Armstrong and Overton’s [74] procedure, in which late respondents are treated as a proxy for non-respondents. A comparison of key demographic characteristics and major constructs between early and late respondents revealed no statistically significant differences (p > 0.05), indicating that non-response bias is unlikely to have materially influenced the study’s findings.

Table 1. Sample characteristics.

Fintech Type	Frequency	Percent	Gender	Frequency	Percent
Mobile payment	69	27.2%	Male	118	46.5%
Mobile remittance	66	26.0%	Female	136	53.5%
P2P lending	62	24.4%
Crowdfunding	57	22.4%
Total	254	100%	Total	254	100%
Age	Frequency	Percent	Education	Frequency	Percent
Under 20	0	0%	Under high school	1	0.4%
20~29	59	23.2%	High school	29	11.4%
30~39	57	22.4%	College/associate	40	15.7%
40~49	73	28.7%	Bachelor	155	61.0%
50 over	65	25.6%	Master	25	9.8%
			Ph.D.	4	1.6%
Total	254	100%	Total	254	100%
Use period	Frequency	Percent	Use frequency	Frequency	Percent
~3 mon. or less	89	35.0%	Once in several months	68	26.8%
3 to 6 mon.	62	24.4%	Once in several weeks	95	37.4%
7 to 12 mon.	39	15.4%	Once a week	22	17.3%
13 to 18 mon.	9	3.5%	Several times a week	44	8.7%
19 to 24 mon.	23	9.1%	Once a day	12	4.7%
More than 24 mon.	32	12.6%	Several times a day	13	5.1%
Total	254	100%	Total	254	100%

shows the sample characteristic.

As shown in Table 1, the response distribution of Fintech types was nearly even: distribution among mobile payment (27.2%), mobile remittance (26.0%), P2P lending (24.4%), and crowdfunding (22.4%). Most respondents used Fintech services weekly (26.0%) or daily (9.8%) within the past year (74.8%). The sample included a large proportion of respondents aged 40–49 years (28.7%), and the majority held a bachelor’s degree (61.0%).

4.2. Development of Measurement

The survey items were developed based on an intensive literature review to ensure content validity. Multiple-item measures for the seven predictors across the three dimensions were adapted from prior innovation and the IS literature to assess: (1) the three innovation attributes in the innovation dimension (e.g., relative advantage, meaningfulness, and perceived risk), (2) the two attributes of the financial service dimension (e.g., structural assurance and trust in transactions), and (3) the two attributes in the IT dimension (e.g., system and information qualities). Fintech use, as the outcome variable of our research model, was measured using three measurement items from Cheng, et al. [75] and Lee [76]. The two contextual factors—use period and use frequency—were measured using ordinal scales, each with six categories. All seven constructs and the outcome variable were assessed using a seven-point Likert-type scale (1 = “strongly disagree”, 4 = “neither agree nor disagree”, 7 = “strongly agree”) (see

Table 2. Assessment of reliability and validity.

Construct	Item	Cron’s alpha	CR	AVE	Loading	T-Statistic
Relative advantage (RA)	RA1	0.807	0.884	0.717	0.827 **	28.634
	RA2				0.859 **	38.233
	RA3				0.853 **	46.946
Meaningfulness (MF)	MF1	0.890	0.931	0.819	0.903 **	63.483
	MF2				0.904 **	66.870
	MF3				0.908 **	60.583
Perceived risk (PR)	PR1	0.854	0.911	0.774	0.858 **	7.306
	PR2				0.905 **	9.616
	PR3				0.875 **	8.389
Structural assurance (SA)	SA1	0.804	0.883	0.715	0.869 **	54.469
	SA2				0.865 **	33.050
	SA3				0.801 **	22.304
Trust in transactions (TRU)	TRU1	0.869	0.920	0.793	0.838 **	36.119
	TRU2				0.905 **	55.445
	TRU3				0.927 **	78.977
System quality (STQ)	STQ1	0.903	0.939	0.838	0.904 **	48.159
	STQ2				0.931 **	75.385
	STQ3				0.910 **	56.627
Information quality (IFQ)	IFQ1	0.872	0.922	0.797	0.841 **	24.867
	IFQ2				0.927 **	97.681
	IFQ3				0.907 **	83.029
Fintech use (FU)	FU1	0.894	0.934	0.826	0.916 **	87.456
	FU2				0.933 **	99.204
	FU3				0.877 **	53.892

Note: ** p < 0.01, AVE: average variance extracted, CR: composite reliability, Cron’s alpha: Cronbach’s alpha.

and Appendix A).

To validate the research constructs, we began with an exploratory factor analysis. The validity and reliability of measurement models were analyzed using SmartPLS 4.0 software. Table 2 presents the results of the factor analysis as well as the reliability and validity tests. The lowest factor loading was 0.801, exceeding the normally used minimum loading value. Next, we assessed the reliability of the constructs using Cronbach’s alpha. The Cronbach’s alpha values ranged from 0.804 to 0.903, all above the recommended level of 0.7 [77]. Convergent validity was evaluated using composite reliability (CR) and average variance extracted (AVE). In Table 2, CR values ranged from 0.883 to 0.939, surpassing the minimum threshold of 0.7. Similarly, AVE values ranged from 0.715 to 0.838, exceeding the acceptable minimum of 0.5. Discriminant validity was confirmed by comparing the square root of the AVE, following Fornell and Larcker’s criteria [78].

Table 3. Correlations of variables.

Constructs	1	2	3	4	5	6	7	8
1. Relative advantage	0.847
2. Meaningfulness	0.669	0.905
3. Perceived risk	−0.223	−0.217	0.880
4. Structural assurance	0.280	0.322	0.163	0.846
5. Trust in transactions	0.347	0.401	0.040	0.699	0.891
6. System quality	0.562	0.562	−0.254	0.166	0.263	0.915
7. Information quality	0.651	0.646	−0.143	0.392	0.461	0.612	0.893
8. Fintech Use	0.582	0.526	−0.159	0.483	0.547	0.468	0.607	0.909

Note: The bold, italicized number is the square root of the average variance extracted (SAVE).

shows that the correlation coefficients among the eight research constructs were all lower than the square root of the AVE for each construct, supporting discriminant validity. We further assessed discriminant validity using the Heterotrait–Monotrait (HTMT) ratio (see

Table 4. Heterotrait–Monotrait ratio (HTMT).

Constructs	1	2	3	4	5	6	7	8
1. Relative advantage
2. Meaningfulness	0.837
3. Perceived risk	0.255	0.204
4. Structural assurance	0.366	0.379	0.221
5. Trust in transactions	0.436	0.453	0.072	0.875
6. System quality	0.774	0.682	0.221	0.198	0.298
7. Information quality	0.776	0.808	0.132	0.469	0.569	0.759
8. Fintech Use	0.700	0.777	0.166	0.571	0.621	0.524	0.761

). The HTMT ratio evaluates whether the correlations between different constructs are significantly greater than the correlations of each construct and their own indicators. Discriminant validity is established if the HTMT value falls below 0.9 in all cases, or below 0.85 for a more conservative threshold [78]. As shown in Table 4, the results showed that all HTMT values were below 0.85, except one (0.875), which still remained under the more lenient threshold of 0.9. These results indicate the that constructs are likely distinct [77,78]. In addition to assessing validity, we assessed multicollinearity among independent variables by calculating the variance inflation factor (VIF). The VIF values ranged between 1.017 and 2.909, which is well within the acceptable range, indicating no serious multicollinearity issues.

Finally, we conducted Harman’s single-factor test to assess common method variance (CMV) [79], and the results indicated no excessive CMV. To further validate these findings, we applied Lindell and Whitney’s [80] marker variable technique as an additional test. The correlations between the marker variable and all latent constructs were below 0.3, suggesting that common method bias is not a significant concern in this study (see Appendix B for a detailed explanation). Collectively, these results confirm that the measurement model is robust and well-supported by the data.

5. Analysis and Results

5.1. fsQCA Approach and Calibration Process

This study adopts the fsQCA methodology to explore the specific configurations of Fintech attributes that lead to high Fintech use. fsQCA is a set-membership analytical technique suitable for analyzing complex configurations [37]. fsQCA is grounded in the principle of equifinality, which posits that multiple, distinct causal configurations can result in the same outcome [33,37,81]. For this study, we used the fsQCA software version 4.0 to identify specific configurations [82].

To analyze our data using fsQCA, the set membership for all variables was calibrated. Calibration is the process of converting all causal conditions (e.g., the seven predictors and two contextual factors—use period and use frequency) and one outcome (e.g., Fintech use) into set membership values ranging from 0 (fully-out of a set) to 1 (fully-in of a set) [33,37,81]. To transform the raw data of each variable into fuzzy-set membership values, we defined three anchors: full membership (fuzzy score = 0.95), the crossover point (fuzzy score = 0.50), and non-membership (fuzzy score = 0.05) [33,82]. Given that this study used a seven-point Likert scale to assess eight constructs (seven predictors and one outcome), we set the value of 6 as the anchor for full membership, 2 for full non-membership, and 4 for the crossover point following guidelines from previous fsQCA studies [11,31,33,83]. To validate the calibration process, we also conducted a sensitivity analysis using an alternative set of values: 1 for non-full membership, 4 for the crossover point, and 7 for full membership. The results were consistent with our original analysis, supporting the appropriateness of our calibration approach. Detailed explanations are provided in Appendix C.

Because the use period and use frequency were measured using a six-category interval scale, they required a different calibration from the other eight variables, defined as a seven-point Likert scale. Therefore, we use a direct calibration method with four anchors. For the use period, we defined values of 0.25 for 6 months or less, 0.5 for ~12 months, 0.75 for ~18 months, and 1 for ~24 months or more, respectively. For the use frequency, we set 0.25 for one time in several months, 0.5 for one time in several weeks, 0.75 for one time or multiple times a week, and 1 for one time or multiple times a day, respectively.

Table 5. Descriptive statistics and their calibration rules.

Measure	Measure Descriptions				Calibration Value at
	Mean	S.D	Min	Med	Max	Fully-in	Crossover	Fully-out
Predictors
Relative advantage	5.31	0.91	2.00	5.25	7.00	6	4	2
Meaningfulness	5.21	0.95	2.33	5.00	7.00	6	4	2
Perceived risk	3.85	1.04	1.00	4.00	6.50	6	4	2
Structural assurance	4.13	0.94	2.00	4.00	6.33	6	4	2
Trust in transactions	4.39	0.88	1.67	4.33	6.67	6	4	2
System quality	5.25	1.03	2.00	5.00	7.00	6	4	2
Information quality	4.98	0.90	2.33	5.00	7.00	6	4	2
Outcome
Fintech use	4.65	0.92	2.25	4.50	7.00	6	4	2
Context factors
Use period	2.64	1.75	1.00	2.00	6.00	-	-	-
Use frequency	4.58	1.36	1.00	5.00	6.00	-	-	-

presents the descriptive statistics for the ten variables and their calibration rules.

5.2. Results of the fsQCA

After the calibration process, we conducted a truth table analysis that logically listed all possible causal combinations of the nine elements based on their calibrated set membership scores [33,37,82,84].

Table 6. Truth table for high Fintech use.

Com.	RA	MF	PR	SA	TRU	STQ	IFQ	Prd	Frq	Freq	High Use	Raw Consistency	PRI Consistency
C1	1	1	1	1	1	1	1	1	1	2	1	0.999	0.997
C2	1	1	0	1	1	1	1	0	1	10	1	0.997	0.992
C3	1	1	0	1	1	1	1	1	1	9	1	0.997	0.991
C4	1	1	1	1	1	1	1	0	1	6	1	0.995	0.984
C5	1	1	0	0	1	1	1	0	1	2	1	0.991	0.969
C6	0	0	1	1	1	1	1	0	1	4	1	0.994	0.969
C7	1	1	0	0	0	1	1	0	1	5	1	0.991	0.967
C8	1	1	1	1	1	1	1	0	0	2	1	0.989	0.964
C9	0	1	0	0	0	1	1	1	1	2	1	0.980	0.817
C10	0	0	1	0	0	1	1	0	1	2	0	0.966	0.746
C11	0	0	0	0	0	0	0	0	1	2	0	0.958	0.391

Note: Com.: combination, Prd: use period, Frq: use frequency, Freq: case frequency.

and

Table 7. Truth table for not-high Fintech use.

Com.	RA	MF	PR	SA	TRU	STQ	IFQ	Prd	Frq	Freq	Not-High Use	Raw Consistency	PRI Consistency
C1	0	0	0	0	0	0	0	0	1	2	1	0.973	0.608
C2	0	1	0	0	0	1	1	1	1	2	1	0.910	0.154
C3	0	0	1	0	0	1	1	0	1	2	1	0.898	0.240
C4	0	0	1	1	1	1	1	0	1	4	0	0.823	0.030
C5	1	1	1	1	1	1	1	1	1	2	0	0.743	0.002
C6	1	0	0	0	0	1	1	0	1	5	0	0.737	0.032
C7	1	1	1	1	1	1	1	0	1	2	0	0.724	0.035
C8	1	1	0	0	1	1	1	0	0	2	0	0.718	0.030
C9	1	1	0	1	1	1	1	1	1	9	0	0.668	0.008
C10	1	0	1	1	1	1	1	0	1	6	0	0.666	0.002
C11	1	0	0	1	1	1	1	0	1	10	0	0.617	0.007

Note: Com.: combination, Prd: use period, Frq: use frequency, Freq: case frequency.

present the truth tables for high and not-high Fintech use. In this research model, the truth table revealed 512 (2⁹) theoretical causal combinations (2^k; k = number of conditions).

To identify the configurations that consistently produced outcomes, a truth table was assessed based on the frequency and consistency of each case [82]. First, we set a threshold for minimum frequency. The frequency column in the truth table refers to the number of users with identical configuration conditions. To differentiate between relevant and irrelevant configurations, a cut-off value for frequency must be established. Generally, a cut-off value of 1 is suitable for small and medium-sized samples, but for large-scale samples (e.g., 150 or more cases), the cut-off should be higher [82]. Given the exploratory nature and sample size of this study, we initially set the acceptable frequency threshold to 3 for high Fintech use [33]. However, no significant configurations were found. Therefore, we lowered the acceptable frequency to 2 for high Fintech use [33]. Second, we determined the minimum acceptable consistency based on the observed consistency distributions in the truth table. We set the minimum acceptable consistency to a cutoff of 0.8 for raw consistency and 0.75 for PRI consistency, in line with the cutoffs recommended by fsQCA studies [11,31,33,39]. This means that only combinations with a raw consistency above 0.8 and a PRI consistency above 0.75 are considered reliable for resulting in high Fintech use. (In the calibration process, the following threshold values were applied: a frequency threshold of 2, a raw consistency cutoff of 0.80, and a PRI consistency cutoff of 0.75.) Table 6 and Table 7 show the truth table results. Each row in Table 6 and Table 7 represent a combination of the attributes within three Fintech dimensions (e.g., innovation, financial service, and IT) and user factors (e.g., use period and use frequency) for high use and not-high use, respectively. Here, 11 rows meet frequency thresholds. Despite the fact that the truth table contained 512 possible rows, only 11 rows were identified as valid configurations in the real-world data because of some empirical restrictions.

Next, a sufficient condition analysis via fsQCA was conducted to investigate the underlying causal patterns [85]. Table 6 describes the fsQCA results using Boolean expressions for intermediate and parsimonious solutions: + means logical OR, * represents AND, and ~ stands for negation. This analysis identified five parsimonious combinations leading to high Fintech use: MF (high meaningfulness), SA (high structural assurance), TRU (high trust in transactions), ~PR*STQ (high system quality without perceived risk), and ~PR*IFQ (high information quality without perceived risk). Additionally, five configurations were found as intermediate solutions for high use. The elements in the parsimonious solutions were embedded in the intermediate solution as core conditions. Elements that appeared only in the intermediate solution were considered as peripheral conditions, complementing the core conditions to enhance Fintech use [33].

Figure 2 graphically represents the results in

Table 8. Solutions for high Fintech use (FU) vs. not-high Fintech use (~FU).

	Parsimonious Solution	Intermediate Solution
High use	MF + SA + TRU + ~PR*STQ + ~PR*IFQ → High use (FU)	RA*MF*PR*SA*TRU*STQ*IFQ*~ UP + RA*MF*SA*TRU*STQ*IFQ*UF + RA*MF*~PR*~SA*STQ*IFQ*~ UP *UF + ~RA*MF*~PR*~SA*~TRU*STQ*IFQ*UP*UF + ~RA*~MF*PR*SA*TRU*STQ*IFQ*~ UP*UF → High use
Not-high use	~RA*~SA + ~RA*~TRU → Not-high use (~FU)	~RA*~MF*~PR*~SA*~TRU*~STQ*~IFQ*~UP *UF + ~RA* ~MF*PR*~SA*~TRU*STQ*IFQ*~UP*UF + ~RA*MF*~PR*~SA*~TRU*STQ*IFQ*UP*UF + → Not-high use

Note: * indicates a combination of two attributes.

using the notation systems of Ragin and Fiss [86]. Each rectangle corresponds to the configuration of an intermediate solution. In this figure, the black circles (●) indicate the presence of a condition, whereas the crossed-out circles (⊗) refer to its absence [33]. The large circles represent the core elements of a configuration, small circles are peripheral elements, and blank spaces indicate a “do not care” condition in which the causal condition may be either present or absent. Core elements are strong causal conditions leading to outcomes, whereas peripheral elements are weaker causal conditions [33].

Figure 2 shows the different configurations in which the core, peripheral, and neutral conditions were identified. We employed two measures, consistency and coverage, to evaluate the fit of each configuration. Consistency describes the extent to which a configuration aligns with the outcome, whereas coverage refers to the proportion of high Fintech use explained by a specific set of configurations, indicating its empirical relevance and importance [82,87]. Specifically, coverage in the truth table is analogous to the R-square value in the correlation-based methods [88]. In addition, raw coverage indicates the proportion of memberships in the outcome explained by each configuration term, whereas unique coverage refers to the proportion explained solely by one solution, excluding memberships covered by other configurations [84]. Our analysis yields five configurations for high Fintech use and three for not-high Fintech use. For high Fintech use, the overall solution consistency was 0.981, indicating that a significant portion of the outcome is explained by these five solutions. These solutions collectively accounted for 64.3% of the cases exhibiting high Fintech use. However, three configurations for not-high Fintech use had an overall solution consistency of 0.885, with a solution coverage of 0.557.

H1 represents a configuration with a high level of all seven attributes, regardless of the use period and use frequency. Therefore, it can be seen as a general solution for achieving high Fintech use. H2 has the highest coverage (0.537), making it the most empirically relevant among the identified configurations, meaning that a large number of users whose answers align with H2 prefer to use Fintech. Notably, although H2 shares a structure similar to H1, it differs in that it lacks perceived risk and has a high level of use frequency, which contributes to its higher coverage compared to the other configurations. H3a and H3b were considered as one solution, H3, because their configurations shared the same core conditions but had different peripheral conditions [31]. This notion was adopted to highlight a general pattern, where each pattern may have multiple minor variations in structures, with the same core elements but different peripheral elements [33].

To make strategic decisions based on users’ personal factors, we classified our configurations into different user groups, considering two key factors (Fintech use period and use frequency). We found that all configurations, except for H1 (i.e., H2, H3a, H3b, and H4), differed depending on these user factors. Despite having a high use frequency, H4 showed the lowest raw coverage (0.203) among the five solutions, indicating that it was associated with a short use period and low use frequency. H3a correlates with high Fintech use within a short period and high use frequency. Although a long use period and high use frequency are present in H3b, its low raw and unique coverage (0.220 and 0.009, respectively) flag it as a solution with a short use period and a high use frequency, similar to H3a. We also decided to regard H2 as a configuration with a long period and high frequency of Fintech use because it showed the “do-not-care” condition for use period with the highest raw and unique coverages (0.537 and 0.149, respectively) among all the identified configurations.

We conducted an additional sufficient condition analysis for not-high Fintech use. As shown in Table 6, two parsimonious solutions and three intermediate solutions lead to not-high Fintech use. The absence of relative advantage, structural assurance, and trust in transactions is present in all configurations, implying that they are central to not-high Fintech use. Because the three configurations share the same core elements but have different peripheral elements, we labeled them L1a, L1b, and L1c [31,33]. In L1b and L1c, system and information quality are peripheral conditions that contribute to not-high use. The result shows that IT alone cannot guarantee high Fintech use despite it being a core condition. Interestingly, we also observed that L1c for not-high use had the same structure as H3b for high use. This isomorphic pattern between H3b and L1c indicates that Fintech companies can produce high use and not-high use simultaneously, depending on how they emphasize their attributes. We next explain the key findings from our empirical results and suggest overarching theoretical propositions about the configurations for achieving high Fintech use.

6. Discussion and Implications

6.1. Discussion of Findings

In this study, we identified nine attributes of Fintech use, including seven key attributes of the three Fintech dimensions and two user factors. We then conducted an empirical analysis to identify specific configurations leading to high use and not-high use. Using fsQCA, we found five different combinations of Fintech attributes for high use and three for not-high use, indicating the existence of multiple equifinal pathways to the outcomes.

Table 9. Fintech solutions by four user types based on two user factors.

		Use Period
		Short	Long
		Infrequent users	Lurkers
Use frequency	Low	~RA & ~MF & PR & SA & TRU & STQ & IFQ (Solution H4)	No configuration found
		Task-driven users	Power users
	High	RA & MF & ~PR & ~SA & STQ & IFQ (Solution H3a) ~RA & MF & ~PR & ~SA &~TRU & STQ & IFQ (Solution H3b)	RA & MF & SA & TRU & STQ & IFQ (Solution H2)

presents the solutions for high Fintech use across four user types based on use period and use frequency.

Given that H1, which includes a high level of all seven attributes, achieves high Fintech use regardless of user factors (use period and use frequency), it can be considered a general solution for achieving high Fintech use. However, if a Fintech company can achieve high use without attaining all the attributes, pursuing a high level of all attributes may be unnecessary and inefficient [39]. Therefore, alternative solutions should be explored to effectively achieve high Fintech use. To conceptually explain our findings and develop a context-specific middle-range theory, we categorized Fintech users into four groups based on two user factors (short or long period and low or high frequency of Fintech use) following Brandtzæg’s [89] user typology: “Infrequent users”, “Lurkers”, “Task-driven users”, and “Power users”. As shown in Table 9, we mapped our empirical configurations to these four user groups. No configurations were identified for the Lurkers group (long use period and low use frequency). Based on our findings, we propose five theoretical propositions.

The Infrequent users (cell 1) have a short period and low frequency of Fintech use, characterized by low interest, little use, and limited experience [89] with Fintech. This group often represents the early stages of technology adoption. Given the significant challenges that Fintech companies face in retaining existing users and attracting new ones, this group contains important potential users of Fintech. Although they perceive Fintech as risky, their perception may reflect a generalized uneasiness about adopting and using new services among individuals with limited or unclear knowledge about Fintech. Brandtzæg [89] noted that while the Infrequent users are not very active, they are “socially curious” and tend to sporadically check whether anyone they know has used a service. Thus, if certain attributes of Fintech can attract their attention and satisfy their curiosity enough to overcome their perception of risk, they might become more inclined to use Fintech.

H4 suggests that structural assurance and trust in transactions are crucial factors for this group, whereas relative advantage and meaningfulness are absent and perceived risk is present. Why are structural assurance and trust in transactions particularly important to the Infrequent group compared to the others? One plausible explanation is that this group seeks reassurance from institutional structures and processes to alleviate their uneasiness about adopting new technologies. These users are more likely to adopt and use Fintech when they have strong trust in institutional structures and transaction processes that align with their values. Therefore, Fintech companies can better engage this group by establishing trust through reliable and trustworthy institutional structures and transaction processes. To achieve this, system and information quality help build trust in the Fintech service. High-quality IT infrastructure can reduce perceptions of risk and make Fintech easier to use. Because of the Infrequent users’ low activity but high social curiosity, encouraging them by showcasing superior system and information quality can help build their trust. In turn, this could help Fintech companies meet the required high levels of structural assurance and trust in transactions, driving higher Fintech use. Therefore, the following proposition is suggested:

Proposition 1:

For users with a short period and low frequency of Fintech use, who perceive it to be risky, achieving high Fintech use requires placing strong structural assurance and trust in transactions at the core of the configurations, facilitated by system and information quality.

No configuration was found in the Lurkers group, cell 2. Lurkers typically take time to observe services and are often the largest user group, although their engagement with the service is minimal. They are commonly found on social networking sites and user-generated content platforms, serving as the window shoppers of the web. They tend to use digital services to pass the time and consume entertainment, rather than for social interactions [89]. The absence of a configuration for the Lurkers group may be caused by a mismatch between the practical purposes of Fintech (e.g., low cost, convenience, fast service, peer-to-peer transactions, and financial information acquisition) and the goals and behaviors of Lurkers, who prioritize entertainment over utility.

Cell 3 in Table 5 maps to Task-driven users, characterized by a short period and high frequency of Fintech use. These users are tech-savvy, using services primarily for utility and information acquisition [89]. They typically have high levels of PC and mobile use, good IT access, and a higher education level, with a greater likelihood of being male [89]. In the Fintech context, this user group expects to gain financial benefits and maximize efficiency while using Fintech. As such, Fintech companies should prioritize Task-driven users, because they are likely to become Power users who demonstrate loyalty to Fintech. As shown in cell 3 of Table 5, the H3 configuration (H3a and H3b) applies to Task-driven users. This configuration includes the core condition of meaningfulness, system quality, information quality, and the absence of perceived risk, while structural assurance and trust in transactions are irrelevant for high use. H3 represents Fintech users who consider the absence of perceived risk crucial for achieving their practical goals. Because this group tends to be focused on the outcomes of their actions, they are particularly sensitive to financial losses and damage, making them highly risk-averse when using Fintech. Why is strong IT, coupled with no perceived risk, so essential for Task-driven users? One reason could be that they frequently use Fintech to achieve their practical objective, and therefore, they rely on robust IT systems that are user-friendly and offer diverse functionalities. To mitigate perceived risks, they believe that strong IT can both alleviate their concerns and meet their needs effectively. Thus, for Task-driven users to adopt Fintech, they require high levels of meaningfulness, system quality, and information quality without perceived risk. Interestingly, relative advantage and structural assurance are “do not care” conditions for this group. Based on these insights, we suggest the following proposition:

Proposition 2:

For users with a short period and high frequency of Fintech use, whose primary focus is gaining financial benefits and utility, to achieve high Fintech use, meaningfulness is crucial, and strong system and information quality should be present while perceived risk is suppressed.

Cell 4 maps to the Power users, who have a long period and high frequency of Fintech use. This group engages in almost all activities provided by a new service [89]. They are typically young males, who represent the most frequent users and show the most varied usage patterns [89]. This group uses Fintech across a wide range of technologies for different purposes, such as mobile payment, equity financing, mobile banking, research and data acquisition, lending, remittance, and crowdfunding. Fintech companies should work to retain this group because they represent the most valuable and loyal users. The Power user group in cell 4 showed one pathway (H2) to reach high Fintech use. H2 represents more users in the “long period and high frequency” group in cell 4 than in the “short period and high frequency” group in cell 3 because H2 has the highest coverage among the identified configurations. H2, which requires a high level of all attributes except for perceived risk, seems to be a general solution for Power users. Specifically, meaningfulness, structural assurance, and trust in transactions are core conditions, and relative advantage and system and information quality are peripheral elements for high use. The presence of perceived risk does not matter in H2. In other words, to satisfy and maintain the loyalty of the Power user group, Fintech companies should work to provide overall attributes in all three dimensions (innovation, financial services, and IT) without worrying about perceived risk. Therefore, we offer the following proposition:

Proposition 3:

For users with a long period and high frequency of Fintech use who have varied usage patterns, to achieve high Fintech use, high levels of all Fintech attributes are generally required regardless of perceived risk.

For all Fintech users, we find evidence of a substitution between financial service attributes (structural assurance and trust in transactions) and IT attributes (system and information quality) for high Fintech use depending on the presence or absence of perceived risk. That is, in a low-risk environment (the absence of perceived risk), strong system and information quality are sufficient to achieve high Fintech use without structural assurance and trust in transactions. However, in a high-risk environment (the presence of perceived risk), high levels of structural assurance and trust in transactions, supported by system and information quality, are required to achieve high use. In both high- and low-risk environments, meaningfulness should be necessary for high Fintech use. For example, although they do not appear in the same user group, H1, H2, and H4 configurations all indicate that a high level of structural assurance and trust in transactions through IT systems are needed for high use when perceived risk is present. However, H3a and H3b require high levels of system and information quality for high use when perceived risk is absent. These results demonstrate the substitutional relationship between financial service attributes and IT attributes, depending on the presence or absence of perceived risk. Therefore, we propose the following:

Proposition 4:

For all Fintech users, in a low-risk Fintech environment, strong system and information quality alone can achieve high use. However, in a high-risk environment, strong structural assurance and trust in transactions, supported by system and information quality, are required to achieve high use. Regardless of the risk environment, meaningfulness is a necessary condition for achieving high Fintech use.

In this study, we identified five different configurations for high use and three for not-high use. Among them, we uncovered one isomorphic configuration that can achieve both high use (H3b) and not-high use (L1c). The H3b configuration for high Fintech use requires the presence of meaningfulness, system quality, and information quality and the absence of perceived risk as core conditions, and the absence of relative advantage, structural assurance, and trust in transactions as peripheral conditions. In contrast, for not-high Fintech use, the absence of relative advantage, structural assurance, and trust in transactions are core elements of L1c, while the absence of perceived risk and the presence of meaningfulness, system, and information quality are peripheral conditions. In other words, H3b and L1c share the same configurational structure, but the ranking of attributes (core or peripheral) differs, suggesting that Fintech companies can achieve either high use or not-high use depending on how they emphasize their attributes.

Furthermore, we observed the multifaceted role of IT across the identified configurations. While prior studies have highlighted the key role of IT in Fintech, few have empirically examined how IT influences Fintech use. We found that system and information quality are present in all configurations for both high and not-high Fintech use, except for L1a, indicating that strong IT alone does not guarantee high Fintech use. As shown in Figure 2, system and information quality combine with other Fintech attributes and play different roles in multiple pathways toward high Fintech use. For example, system and information quality are core conditions in two of the five configurations (H3a and H3b), with no perceived risk, but they support and complement strong structural assurance and trust in transactions in H1, H2, and H4. System and information quality are peripheral conditions for not high-use in two of the three configurations (L1b and L1c). For example, high-quality information, such as real-time transaction alerts and transparent fee disclosures, can mitigate perceived risk, especially for users engaged in frequent, short-term tasks. While robust IT infrastructure is foundational, achieving meaningful outcomes also necessitates the integration of additional factors like user trust, regulatory support, and organizational culture. IT attributes may or may not lead to high Fintech use, depending on which attributes they are combined with. Park and Mithas [32] suggest that the presence of IT alone is neither necessary nor sufficient for achieving high performance in any configurations. In our results, system and information quality are critical components for high use, but they play multifaceted roles in combination with other attributes, ranging from core or enabling to even counterproductive, depending on the context. Thus, we propose the following:

Proposition 5:

System and information quality play multifaceted roles, ranging from core or enabling roles to counterproductive roles, depending on how they interact with other Fintech attributes. Therefore, harmonizing these IT attributes with other key Fintech attributes is crucial for achieving high Fintech use.

Table 10. Summary of Fintech solutions for the four user groups.

User Type	Key Task	Management Actions
Infrequent users: High-risk perception (Short-term + Low frequency)	Trust in transaction & structural assurance	Provide strong security and legal safeguards; ensure safe and reliable transaction experiences
Task-driven users: Financial benefit-oriented (Short-term + High frequency)	Meaningfulness & IT quality	Maximize financial benefits, improve information quality, and minimize perceived risk
Power users: Diverse use pattern (Long-term + High frequency)	Comprehensive attributes	Offer all-in-one services, provide personalization, and strengthen overall IT quality
All users: Depending on risk environment	Context-specific response	Low-risk → strengthen IT quality; High-risk → enhance structural assurance and trust in transactions; In all cases → ensure meaningfulness
Common factor: IT quality	Multifaceted roles	Integrate IT quality harmoniously as a core, enabling, or coordinating factor

presents the summary of Fintech solutions for each user type, derived from the five propositions of this study. It provides managers and practitioners with a structured basis for developing precise and effective strategies that account for the characteristics of different user segments, while also offering practical insights into promoting Fintech adoption and supporting sustainable growth.

6.2. Theoretical and Practical Implications

Previous research on Fintech primarily focused on examining individual antecedents of Fintech use, which has led to a limited understanding of the Fintech phenomenon. This limitation stems from a method that tests the effects of these antecedents separately rather than considering them in combination or as part of a holistic framework. In this study, we address these research gaps by offering the following theoretical contributions: First, we adopt a holistic approach, providing a more integrated and realistic perspective on Fintech use. Our approach demonstrates how a combined set of antecedents can lead to high Fintech use, offering a more comprehensive understanding. This approach has received limited attention in the IS literature despite its significance. Our research contributes to extending IS research by highlighting how valuable this method can be for understanding complex phenomena, particularly those that involve multiple interconnected dimensions.

Second, we identify three key Fintech dimensions based on established definitions of Fintech in the literature and develop a theoretical framework that captures the multidimensional nature of Fintech. Because the Fintech phenomenon is relatively new, and previous research has primarily focused on individual antecedents, no comprehensive theoretical framework has yet been developed to address this concept. Our theoretical framework offers a clear structure and scope for this research, providing a solid theoretical foundation. Therefore, the Fintech dimensions we identified and the theoretical framework we propose can contribute to laying the groundwork for future research in the Fintech field.

Third, this study advances the IS literature by explaining the interdependencies among Fintech attributes and their combined effects on Fintech use. Previous IS studies have explained technology adoption and use by investigating the effects of various antecedents on user behavior but have overlooked the interconnected causal relationships among these variables [88]. To identify the interdependent and interrelated relationships within the Fintech context, we propose a theoretical framework that combines three Fintech dimensions and explores the interdependence among these attributes to test their influence on Fintech use. Our study makes a significant contribution by theoretically and empirically identifying various combinations of Fintech attributes and examining the relationships between specific attribute combinations and Fintech use.

Fourth, this study attempts to overcome the limitations of traditional statistical techniques in the IS literature by using fsQCA in the Fintech context. The fsQCA has often been applied at the organizational level in the IS field to identify different configurations of constructs, but it has rarely been used at the individual level [11,31,34,39,87]. As qualitative comparative analysis can capture the complexity of user decisions regarding technology use [31], we employed fsQCA to examine user decisions regarding Fintech use. This study represents an early attempt to investigate individual-level user behavior in the IS field through a configuration analysis.

The results of this study have several practical implications. First, they can help Fintech managers establish multiple effective alternative ways to strengthen Fintech use. These findings indicate that specific combinations of attributes lead to increased Fintech use. By identifying the multiple Fintech innovation configurations that enhance Fintech use, Fintech managers can develop more effective, personalized strategies, ensuring the long-term success and sustainability of their companies. Furthermore, the solutions identified in this study reflect different customer perceptions of Fintech use, offering practical insights for advanced customer profiling. Based on these findings, Fintech managers can develop launch strategies for different potential and existing users by leveraging various combinations of Fintech attributes.

Second, this study provides insights into the critical role of IT in driving high Fintech use. As previously mentioned, IT is a key factor in achieving business success in Fintech. Using fsQCA, we explored the role of IT within the context of Fintech. Our findings reveal that IT is necessary across all solutions for both high and not-high Fintech use. However, the results also show that IT alone does not guarantee high Fintech use: rather, it must consistently interact with other main Fintech attributes to increase Fintech use. This study is one of the first to empirically identify the multifaceted roles—core, enabling, and counterproductive—that IT plays in relation to other Fintech attributes for achieving high Fintech use. Consequently, Fintech companies should focus on developing and offering innovative Fintech along with high-quality IT to foster Fintech use.

Third, this study offers a new approach for Fintech managers to improve their service development processes. Given the experiential nature and complexity of new services, relying on a single service attribute is no longer sufficient to create the “best” market profile [11]. For example, striving for high levels across all Fintech attributes may be inefficient. Instead, depending on the type of Fintech user, more effective alternatives are required to achieve high Fintech use. By utilizing the fsQCA, Fintech managers can develop alternative strategies tailored to different user types by combining individual attributes to enhance Fintech use throughout the development process. Adopting a holistic approach allows for the creation of multiple potentially successful combinations, which help reduce the uncertainties and risks associated with new service development during its early stages [11].

Last, our findings suggest that Fintech use pathways can serve as strategic levers to advance financial inclusion, enhance financial stability, and promote long-term sustainability. Fintech expands access for underserved populations by lowering entry barriers through mobile banking and digital payments [90,91]. It also strengthens system stability by improving transparency and efficiency with digitalized processes and blockchain. Moreover, ESG-oriented investments and green finance initiatives create opportunities for responsible practices and sustainable resource allocation, aligning business strategies with societal goals [91,92,93]. These findings underscore Fintech use pathways as strategic mechanisms that link financial inclusion and stability with broader sustainability objectives.

6.3. Limitations and Future Research Directions

Despite its valuable contributions, this study has several limitations that must be acknowledged. First, while fsQCA is well-suited for examining the causal complexity of Fintech adoption and use, it has a weakness in that the calibration process can be influenced by the researcher’s subjective judgment. Meijerink and Bondarouk [94] highlighted that using knowledge from diverse data sources and domains is crucial for minimizing potential bias during calibration. Therefore, future fsQCA studies should incorporate multiple data sources from various domains to address this issue. Second, the small sample size in this study may have resulted in the omission of potential configurations, as small samples can limit the ability to identify all possible theoretical configurations in the truth table [82]. Future research with larger sample sizes is necessary to uncover additional configurations that may have been overlooked in this study. Third, the generalizability of our findings is limited, because the inclusion or exclusion of certain conditions can substantially alter the configuration in fsQCA [11]. While we identified five solutions for high Fintech use and three solutions for not-high Fintech use based on seven innovation attributes across three Fintech dimensions and two user factors (use period and frequency), other relevant innovation attributes may have been omitted. Thus, future studies should explore additional attributes (e.g., complexity, self-efficacy, perceived cost, and service quality) and evaluate how specific combinations of these attributes influence Fintech use.

Fourth, this study focuses on the combination of causal variables in Fintech innovation but does not explore the causal relationships between these variables. The IS literature discusses the various roles of IT as a trigger, enabler, and innovator in innovation; however, our study concentrated on the simultaneous and systematic combinations of Fintech innovation attributes, without investigating the causal links between IT and other attributes. Future empirical studies should address these causal relationships. Fifth, although we empirically identified five configurations for high Fintech use and three for not-high Fintech use, we did not explore the theoretical implications of these specific configurations in depth. Further theoretical exploration is needed to address this gap.

Sixth, although this study aimed to capture individual-level perceptions of risk and trust, the items used inevitably reflected institutional and firm-level attributes. Unlike traditional IT services, the adoption and use of Fintech are strongly influenced by macro-level factors, such as the regulatory environment and market-wide trust, which are often inseparable from individual user perceptions. This creates a fundamental measurement challenge in the Fintech domain: how to separate personal beliefs from the broader systemic conditions that shape them. Despite our efforts, the possibility of such ambiguity remains and constitutes a limitation of this study. Future research should address this issue by developing measurement items that more clearly differentiate between individual-level perceptions and firm- or system-level attributes. Seventh, although the four types of Fintech differ in terms of user needs and risk structures, this study did not explore their causal relationships because a detailed analysis of this issue would exceed the scope of the present study. Therefore, future empirical studies should examine specific causal relationships across more diverse types of Fintech. Finally, the generalizability of our findings is limited to the South Korean context, as the survey data were collected exclusively within Korea and may limit the generalizability of the findings to other cultural, institutional, and market contexts. Therefore, caution is required when interpreting the results beyond this national setting. To enhance the robustness and external validity of the conclusions, future research should examine Fintech use behavior across diverse countries and cultural contexts at the individual level, thereby providing more comprehensive and globally relevant insights.