Understanding Consumers’ Barriers to Using FinTech Services in the United Arab Emirates: Mixed-Methods Research Approach

Abstract

The cutting-edge development known as FinTech is now fast replacing traditional financial services all over the world. Despite that, UAE consumers are still not embracing FinTech services at the expected rate. This study hence suggests expanded research based on the unified theory of acceptance and use of technology (UTAUT) to deeply examine the obstacles preventing consumers from using FinTech services. This research utilised an exploratory sequential mixed-method approach. Preliminary semi-structured interviews involving ten banking experts were undertaken to explore the barriers preventing consumers from using FinTech services. To get additional empirical support for the research concept, the study sequentially examined numerous components using a quantitative cross-sectional online survey involving 332 bank customers. The qualitative investigation highlighted six new barriers that consumers face when using FinTech. Through quantitative data analysis, the preliminary qualitative findings were largely verified. As far as the authors are concerned, this inquiry is the first to put forth a thorough model that takes into account organisational, technological, individual, and environmental aspects for addressing the problem of low FinTech usage. By incorporating several new factors, this study also expands the UTAUT. Additionally, it is one of the first studies to examine FinTech adoption employing a mixed-approach methodology.

1. Introduction

Banks and other classic financial institutions, which centralised market power in the financial system, have controlled financial services for decades. Nonetheless, the financial sector is now undergoing a disruptive structural transformation in the fourth industrial revolution (IR 4.0) era as a result of numerous technological advancements, together with the COVID-19 pandemic that has accelerated the process to stimulate big tech corporations [1,2] such as Amazon, Google, Meta, Microsoft, and Alibaba to proactively participate in the financial system. This led to the conception of Financial Technology (FinTech), a financial innovation made possible by technology that affects financial markets, institutions, and the provision of financial services via the introduction of new business models, applications, procedures, or products [3].

FinTech is a cutting-edge innovation displacing traditional financial services and is rising to prominence globally. There have been over 12,500 start-ups in FinTech, with a global investment of USD 111.2 billion in H2′2022 [4]. The global FinTech market is anticipated to grow steadily and generate USD 324 billion in market value by 2026, with an increasing compound annual rate of 25.18% over the 2022–2027 forecasted period, due to the currently vast numbers of smart device users who prefer online transactions along with the evidence that FinTech implementation significantly boosts customer experience [5].

The significant advancements in information technology (IT) and their integration, including the internet of things, artificial intelligence, big data, cloud computing, and blockchain have allowed financial services firms to automate their business processes and fundamentally rationalise the financial services value chain with entirely new and inclusive products, services, processes, and business models that can effectively fulfil the needs and demands of users [6,7]. Academic studies back up the idea that FinTech services give consumers access to a dynamic ecosystem because they offer personalisation, flexibility, and simplicity of delivery at a reduced cost, which ultimately boosts productivity, profitability, and financial inclusion [4,7]. Besides its qualities that promote the United Nations’ Sustainable Development Goals (SDGs) [8,9], FinTech can make financial businesses more sustainable by promoting green finance [10]. Additionally, FinTech potentially gives access to financial services to 1.6 billion people in emerging economies. By minimising expenditures and tax revenue leakage, it may boost the amount of loans made to people and businesses by USD 2.1 trillion while also enabling governments to save USD 110 billion annually. It is equally advantageous for financial service providers, who could sustainably increase their balance sheets by up to USD 4.2 trillion while saving USD 400 billion yearly in direct costs [11]. Substantial FinTech usage can enhance emerging economies’ GDP by USD 3.7 trillion by 2025, or 6% more than the status quo [11].

The government of the United Arab Emirates (UAE) continues to place a high premium on digital transformation. The UAE’s central bank established a FinTech unit in December 2020, emphasising its dedication towards establishing proper regulations, privacy and data protection, low carbon and green FinTech, and inclusive financial services, as well as towards developing a mature FinTech ecosystem [12]. The UAE is leading the MENA’s FinTech market, recording a high of USD 2.5 billion with investment growth of 64% reaching USD 819 million in 2022 [13]. The number of FinTech companies in the UAE is steadily rising. In 2022, there were 189 new licensed FinTech companies taking the total to 303, offering various financial services, including e-payments/transactions, e-wallet, blockchain/cryptocurrency, digital banking/neobanks, InsurTech, WealthTech, RegTech, crowdfunding, peer-to-peer insurance and lending platforms, remittance, and others [13]. Investors are now encouraged to help regional projects financially thanks to FinTech platforms, hence promoting UAE’s 2030 vision to become a regional and global hub for FinTech and contributing to the country’s overall economic growth [12].

Across the globe, FinTech services adoption has seen a remarkable increase among consumers from 16% in 2015 to 33% in 2017 and 64% in 2019, with the high adoption rate mainly in nations such as India and China [14]. However, the UAE has experienced a relatively poor consumer adoption rate, as low as 29% [15]. Despite the abundance of FinTech options that are accessible, adoption is highly selective, and only a small number of these have been a success. An example is e-payment services used by 84.3% of users, fuelling the growth in the usage of FinTech services [16]; others have shown lower adoption, including P2P money transfer [31%], robot advisor [27%], InsurTech [19%], crowdfunding [17%], and P2P insurance [10%], according to the national survey by Statista (2020) [15]. This duality presents the possible issues or obstacles in the use of FinTech. This study is thus inspired to look into the issues preventing clients in the UAE from utilising the existing FinTech services. The diffusion of FinTech is essential in preventing the most disadvantaged segments from significant financial losses, falling behind, attracting potential users, and retaining existing consumers.

For the purpose of marketing technology services in emerging areas, comprehension of the diffusion process is essential. Rogers [17] asserted that potential users’ readiness to embrace technological innovation is key to ensuring technology’s success and widespread adoption. Yet, limited comprehensive research findings have identified the factors influencing the use of FinTech [3]. Several studies examining the obstacles to the adoption and application of FinTech were found in the recent systematic literature analysis [18], most of which focused on the payment sector. Studies have been conducted to evaluate the FinTech phenomenon [19,20,21,22,23,24,25]. They mainly concentrated on particular characteristics that are personal attributes of clients. However, they failed to take into account the individual, technological, organisational, and environmental characteristics that, when taken together, would provide a solid theoretical foundation for fully comprehending consumers’ perceptions [26,27]. The technology acceptance model (TAM) has been heavily cited in the literature by numerous researchers looking into how FinTech services are being adopted. The unified theory of acceptance and use of technology (UTAUT), which is regarded as a solid motivational basis characterising consumer behaviour towards technology, has received little empirical support [25]. Thus, a theoretical need was identified in the literature to broadly explore the challenges affecting consumers’ FinTech usage based on the UTAUT model. Contextually, the existing studies have been mainly experimented within East Asia. Their findings might not be practical in diverse Middle Eastern contexts such as the UAE due to the prevalence of distinctive consumer behaviour, cultural settings, social infrastructure, and economic indicators. As a result, it was determined that studies on the adoption of FinTech should be performed for each country. The majority of authors took a positivist approach to research designs by merely employing cross-sectional surveys to validate an altered research model. Their determinants were created by synthesising prior research and accepted hypotheses. As they ignored the mixed-method approach that merges the strengths of quantitative and qualitative approaches in a single study to determine the methodological contributions, most models were therefore primarily classified as restricted and tactical.

The study aimed to address the following research questions (RQs) in order to close the aforementioned research gaps:

RQ1: What challenges affect consumers’ usage of FinTech services in the UAE?

RQ2: What effects do individual, technological, organisational, and environmental factors have on consumers’ intention to use FinTech services?

RQ3: Is the UTAUT model relevant for explaining consumers’ use of FinTech services in the UAE?

Unlike the extant studies, this study essentially is a fresh attempt to bridge the identified theoretical, methodological, and contextual gaps by exploring the obstacles preventing consumers from using FinTech services using a mixed-method approach and extending the UTAUT framework in the UAE. The FinTech literature can benefit from the advancements made by this study. First, it facilitates identifying the numerous issues affecting the uptake of FinTech services among UAE consumers. Second, it employs and experimentally extends the UTAUT model in forecasting the uptake of FinTech services, particularly to the applicability and generalizability of the UTAUT in new contexts. Third, the study uses both qualitative and quantitative approaches in a mixed-method approach, thus paving the way for a clearer understanding of the intricate interrelationships between the new elements influencing the uptake of FinTech services. Lastly, the results of this study provide insightful perspectives for researchers and help managers and policymakers to create successful plans for influencing consumers’ digital usage behaviour.

2. Literature Review

2.1. FinTech: A Portmanteau of Finance and Technology

A new wave of technological innovations, known as FinTech, is regarded as a differentiating taxonomy that primarily defines the financial technology sectors in various activities, essentially concerning the enhancement of service quality via a heavy reliance on IT solutions [28]. Many definitions of FinTech have been given in the literature, although they do not differ considerably. It alludes to companies that operate beyond conventional financial services via the utilisation of technology, with business models that alter the provision of financial services [4]. As per the working definition of the International Monetary Fund (IMF), FinTech is a broad range of financial services that rely on digital channels for their delivery and access [29]. FinTech refers to cutting-edge financial solutions made possible by technological innovation. It is frequently used to refer to start-up businesses that provide these solutions. Still, it also includes established financial service providers such as banks and insurers to boost their competitiveness and improve financial functions and consumer behaviour [4,7]. As an umbrella term, FinTech is the application of digital technology to financial services [30], covering immense scopes of techniques, including data security and financial service delivery. In the current study, FinTech describes technology-related innovations used in designing and delivering financial services and products.

2.2. The Unified Theory of Acceptance and Use of Technology (UTAUT)

Using well-established theoretical frameworks drawn from various socio-cultural contexts, usage intentions and user behaviour have been studied. While the current ones have been and continue to be utilised, validated, modified, or criticised, new frameworks are being developed to address the shortcomings/insufficiencies present in the current ones [31]. The TAM and its derivatives (TAM2 and TAM3), the Social Cognitive Theory (SCT), the Diffusion of Innovations Theory (DOI), and the UTAUT are prominent among the theories/models used for gaining insights into people’s and organisations’ likelihood to embrace, adopt, and employ technology. TAM-based and UTAUT models have been determined to be the most effective among these frameworks [32]. Owing to its highly explanatory and predictive components, the UTAUT was shown to be the most effective model for explaining technology adoption with 70% variance [33].

According to the systematic review [33], the UTAUT model is reliable for examining people’s adoption of IT in various fields. However, when attempting to describe how users embrace IT, particularly in consumer scenarios, the UTAUT model’s explanatory capacity may be constrained [34]. According to Venkatesh [35], the addition of new determinants could aid in broadening the UTAUT’s applicability in a consumer environment. In light of this, the primary contribution of the current study is not only to replicate it in a new setting but also to extend the UTAUT model by adding new individual, technological, organisational, and environmental determinants. This creates a solid foundation to thoroughly explain consumers’ intention to use FinTech in the context of emerging economies such as the UAE.

3. Research Methodology

The study technique was developed using the mixed-methods approach. A mixed-methods strategy minimises the weaknesses of both qualitative and quantitative methods while combining their advantages in a single study [35]. The various research procedures enable the researcher to accept, cross-validate, and verify findings within a single study [35,36]. Therefore, this study adopted an exploratory sequential mixed-methods approach to investigate the barriers preventing UAE’s consumers from using FinTech. In order to get a comprehensive understanding of the subject, the study first reviewed pertinent literatures. Using the information gathered during the preliminary stage of the semi-structured interviews, the authors identified the empirical codes that most likely serve as the foundation of the study. This made establishing a core set of variables and put the research in a broader context. A hypothesis was then developed for each empirical code to create the study’s final model. In order to gather further empirical evidence to support the theoretical framework, the study investigated the developed hypotheses via a quantitative cross-sectional survey using a larger sample.

3.1. Phase One: Qualitative Study

A preliminary qualitative study which examined the key elements influencing how consumers perceive FinTech was carried out through semi-structured open-ended interviews with UAE bank managers. Based on the study’s objectives, the researchers created a protocol for the interviews. In this case, study, the factors found in the interview transcripts were categorised using the inductive method. A hierarchical framework was used to develop the conceptualised interpretations, with the top-level notions serving as the primary factors of the study objective. Lastly, a theoretical narrative describes the framework underlying the explanation of these factors.

Focus group interviews served as the primary data-gathering method for this study. The main goal of qualitative research is to amass comprehensive knowledge about a subject. As advised by many qualitative scholars, a purposive sample strategy was utilised here to choose the suitable informants, namely the specialists, and to comprehend the studied phenomenon [35,36,37,38]. Most contemporary qualitative academics have noted that the researcher’s subjective assessment defines the sample size and that the researcher is aware of when the saturation point has been achieved [35,36,37,38]. When the material has reached the point of saturation, and no new themes are coming to light, the process can be terminated [36,38]. As such, the semi-structured interviews with ten individuals from varied positions and sites were sufficient in this study to accomplish saturation. The semi-structured interviews were conducted between November 2021 and February 2022, and each interview took about 45 to 60 min.

The transcriptions were analysed using Thematic Content Analysis (TCA), a qualitative method, to locate the codes related to the theoretical foundation, which served as the basis for the research. The steps outlined by Braun and Clarke [39] were used for the qualitative data analysis. These include conducting the interviews and recording them, listening to the taped interviews and transcribing them, getting participants to confirm the transcripts, coding the verified transcripts, naming and organising the codes, putting quotes and memos into the proper codes, analysing the results and producing outputs, and finally, putting the report into writing.

3.2. Phase Two: Quantitative Study

Based on the semi-structured interviews, several probable elements that might have influenced consumers’ adoption of FinTech were found. The proposed final model from the study was created using a set of hypotheses. After that, to evaluate the developed hypotheses, a quantitative cross-sectional survey was conducted among a larger sample of UAE consumers gathered via an online survey between April 2022 and August 2022. Two phases of the PLS-SEM in Smart PLS 3 were carried out to validate the results, i.e., the measurement model assessment to confirm the instrument’s accuracy and validity and the structural model assessment to test the study hypotheses. PLS-SEM was employed in this study due to its suitability in the exploratory phase of the theory building, prediction or expansion [40].

Meanwhile, power analysis determined the minimum sample size due to the lack of a sample frame. Based on Cohen’s (1988) [41] formula for identifying the most appropriate sample size, G*Power was utilised to determine the standardised significance criterion α, effect size (ES), statistical power (1-β), and quantity of indicators. By utilising G*Power for two tails, medium ES of 0.05, α of 0.05, power of 0.95, and ten predictors, it was found that at least 242 respondents were needed. However, for the purpose of generating more reliable outcomes, the researchers raised the sample size to 332.

To guarantee the validity of all the 53-question instruments, the measurement scales used in earlier studies were modified where needed to meet the setting of the current study. Multiple items with scales ranging from 1 for “strongly disagree” to 5 “strongly agree” were used to measure each construct. Appendix A lists the survey questions and their sources. Despite the fact that the majority of people in the UAE are fluent in English, the original instruments were translated into Arabic by a certified multilingual translator. A second certified multilingual translator retranslated the Arabic version into English. Next, the semantic equivalence between the back-translated and original versions was examined. There were minor differences between the source text and the back-translated version. In addition, during the pre-test, the questionnaire was validated by two senior academic experts and one professional banker to detect any issues with phrasing, content, or ambiguity. As requested by the academic specialists, a pilot survey involving 50 bank customers was conducted. As the reliability of each construct was more than 0.70, the pilot test findings were deemed satisfactory [42,43,44].

4. Phase One: Qualitative Data Analysis and Findings

For the semi-structured interviews, the researcher contacted a number of banking professionals. Ten bankers from different sites in the UAE ultimately consented to take part in the study, which was enough to reach the point of saturation. In order to protect their privacy, the participants were designated as R1, R2, through to R10. The selection of the informants was based on their positions, professional backgrounds, and knowledge of the banking business and this study topic (

Table 1. Informants’ Professional Profiles.

	Bank	Location	Current Position
R1	Dubai Bank	Dubai	Branch Manager
R2	Abu Dhabi Bank	Abu Dhabi	Branch Manager
R3	Abu Dhabi Bank	Ajman	Branch Manager
R4	Noor Bank	Fujairah	Branch Manager
R5	Abu Dhabi Bank	Abu Dhabi	Region Manager
R6	Emirates Bank	Dubai	Strategic Planning Director
R7	Emirates Bank	Dubai	Assistant General Manager
R8	Dubai Bank	Dubai	Sales Officer
R9	Sharjah Bank	Sharjah	Marketing Manager
R10	Sharjah Bank	Sharjah	Sales Manager

).

Six new subthemes in line with the four predetermined themes were produced from the qualitative analysis: the individual attribute was denoted by consumers awareness and personal innovation, the organisational attribute was denoted by firm reputation, the technological attributes were represented by security and privacy and system quality, whilst the environmental attribute was denoted by governmental support (see

Table 2. Summary of the Qualitative Phase Output.

	Sub-Themes Extracted	Participants
Themes		R1	R2	R3	R4	R5	R6	R7	R8	R9	R10	Total Participants	Ratio
Individual Attributes	Consumers Awareness	√	√	√	√	√	√	√	√	×	×	08	80%
	Personal Innovativeness	√	√	√	×	√	×	√	√	√	×	07	70%
Technological Attributes	Privacy and Security	√	√	√	√	√	×	×	×	×	√	06	60%
	System Quality	√	√	√	√	√	√	×	√	×	×	07	70%
Organisational Attributes	Firm Reputation	×	√	√	×	×	×	√	√	√	×	05	50%
Environmental Attributes	Governmental Support	√	×	×	√	√	√	×	√	×	√	06	60%

). By asking the selected subject-matter experts to evaluate the data patterns with respect to the related themes, the researchers were able to validate the study. This demonstrated the degree to which the findings met the initial phase’s objective.

5. Research Framework and Hypotheses Development

The final research framework shown in Figure 1 was developed based on the UTAUT model’s four core constructs as well as six additional factors identified during the qualitative data analysis, including consumer awareness, personal innovation, firm reputation, security and privacy, system quality, and governmental support. These factors were categorised under the four basic attributes of individual, technological, organisational, and environmental. This served as the foundation for investigating to fully explain consumers’ intention to use FinTech services. The next subsection discusses each of the variables proposed in this study and their most plausible connections.

5.1. Original UTAUT Model

5.1.1. Performance Expectancy

A component of the UTAUT model, performance expectancy to predict consumers’ belief in the improvement of task implementation via the usage of technology [45]. It was also identified as the most effective predictor of user behavioural intention in a later revision (UTAUT2) [46]. Many researchers have paid close attention to performance expectancy [47,48,49,50,51]. According to these studies, it is a crucial concept in the analysis of improved information system usage. The degree to which FinTech meets customer expectations is essential to fully understand its usefulness [23,52,53]. Therefore, consumers may be more likely to use these services if they believe FinTech would significantly improve their financial performance. This study hypothesised that performance expectancy would affect consumers’ intention to use FinTech services in line with the assertion of the existing literature and the UTAUT model, which claimed that IT-oriented products or services boost job performance and enable efficient, useful, effortless, and timely transactions. This debate resulted in the hypothesis below:

H1: 

There is a significant relationship between performance expectancy and consumers’ intention to use FinTech services.

5.1.2. Effort Expectancy

Effort expectancy determines users’ level of convenience when utilising certain information systems [45]. Regarding the influence of effort expectation on technological behaviour, prior literatures have reported a variety of contradictory findings. Numerous studies have found that consumers’ intentions are significantly influenced by effort expectancy [23,54,55]. Contrarily, it was asserted in other empirical research that it is not a substantial factor, such as in the adoption of e-learning [56], e-voting [57], mobile commerce [2], and green technology [58,59]. Particularly, contradictory findings have been reported in the context of FinTech. Rahim et al. [52] found an insignificant influence on consumer perception, which conflicts with the conclusion of Yeh et al. [53] who confirmed the relationship. These wildly divergent findings demonstrated the need to investigate how effort expectancies affect users’ intentions to use FinTech services. According to the UTAUT model, consumers might not hesitate to use valuable and convenient IT-based services when making financial transactions. This provided support for the second hypothesis, which proposed that consumers’ expectation of effort would influence their decision to use FinTech services:

H2: 

There is a significant relationship between effort expectancy and consumers’ intention to use FinTech services.

5.1.3. Social Influence

Theories of social psychology stress the significance of social influence in shaping behaviour. According to Bandura and Walters’ [60] social learning theory, people can learn from one another by communicating with reliable contacts. In addition, the conflict elaboration theory of social impact proposed that an individual’s relationships with other group members should be considered while deciding whether to embrace or reject a certain technology [61]. Many theories and models of the adoption and use of technology, including the TRA, TBP, TAM, and UTAUT, contend that social influence is a key factor in determining a person’s behavioural intention. Social influence is the degree to which an individual’s use of technology is influenced by the opinions of others [45]. Despite not supporting technology usage, the theory underlying social influence holds that such people will nonetheless utilise technology if they perceive that it will boost their reputation among their peers [62]. Numerous empirical researchers have found that social influence has a significant impact on user behaviour [26,63,64,65,66]. However, subsequent studies debunked this notion as they found no conclusive evidence to the claim [67,68,69,70]. Likewise, contradictory findings about the function of social influence have been reported in FinTech research. Some studies supported the beneficial effects of social influence [23,52,53,59,71]. Others, however, claimed that it was not a significant factor in determining how consumers felt [58,59,72]. These contradictory results suggest that social influence is highly context-dependent, which motivates the review of the study’s setting. According to the UTAUT model, it can be concluded that some technology services are in style, lending support to the notion that consumers’ intentions to use FinTech services are influenced by the social influence construct:

H3: 

There is a significant relationship between social influence and consumers’ intention to use FinTech services.

5.1.4. Facilitating Conditions

The provision of technical resources needed to facilitate the deployment of a certain technology is referred to as a facilitating condition [45]. According to the UTAUT, facilitating condition is a construct that reflects an individual’s sense of control over their behaviour [73]. Although the extended UTAUT2 corroborated this direct effect, the original UTAUT did not indicate a direct relationship between facilitating conditions and behavioural intention [46]. The research emphasised the significance of favourable circumstances as a critical indicator of intention to employ various technologies [50,74,75,76]. There is still a lack of data from the context of the UAE, a Middle Eastern country which is socially and economically distinct. Practically, facilitating conditions refer to when consumers have adequate supporting resources such as the expertise to embrace FinTech services, easy Internet access, the necessary smart equipment, and expert advice, allowing them to gain insightful ideas. Consequently, the following hypothesis was put forth:

H4: 

There is a significant relationship between facilitating conditions and consumers’ intention to use FinTech services.

5.2. Individual Attributes

5.2.1. Consumer Awareness

The acceptance of innovations requires awareness [77]. The more knowledge is offered on technology properties, innovation is expected to rise [17]. People will eventually become more aware of and driven to support a particular service if they are well-informed about it [78]. A technology’s acceptability is hampered by people’s ignorance of its advantages, limitations, and benefits [79,80]. Numerous academics have emphasised how awareness might affect consumers’ behavioural intention [71,81,82,83,84]. This suggests that offering additional details about the precise characteristics of FinTech services may have a favourable impact on the adopters’ choices. It is expected that consumers with high levels of awareness would be more likely to accept innovative FinTech by taking into account the perception of the informants in the qualitative study as well as the discussion presented above. Consequently, it is hypothesised that:

H5: 

There is a significant relationship between consumer awareness and their intention to use FinTech services.

5.2.2. Personal Innovativeness

According to diffusion theory, adopters have positive expectations about new technologies [85]. Positive attitudes toward technology adoption influence user satisfaction favourably [86], thus encouraging innovation and improving technology usage behaviour [87]. In the sphere of technology, Agarwal and Prasad [88] established the idea of personal innovativeness, i.e., an individual’s disposition to use a certain new technology. Although it was initially proposed as a moderating element [88], personal innovativeness has been shown to be a crucial antecedent in adopting innovations [89]. Numerous empirical investigations have found that a person’s capacity for innovation influences their behaviour [70,90]. This suggests that highly inventive people are better disposed to using a variety of FinTech services. The following hypothesis was developed based on the informants’ expectations in the qualitative inquiry and the prior literatures:

H6: 

There is a significant relationship between personal innovativeness and consumers’ intention to use FinTech services.

5.3. Technological Attributes

5.3.1. Security and Privacy

Security and privacy have become more crucial due to new technologies’ expanding capacity for information processing and integration into consumers’ everyday lives. Due to their growing sense of insecurity on how their personal data are being collected and handled [91], consumers find it difficult to accept their lack of control over their behaviour [92]. Yoon et al. [93] emphasised that security and privacy are conditions for technology adoption, and further highlighted the quantitative significance of this issue. Positive perceptions of security and privacy are associated with a better likelihood to adopt a new financial system [94] and services [26], higher e-satisfaction [95], user trust [2,24], and better service selection [96]. Meanwhile, Chatterjee [97] and Bouteraa et al. [58] asserted the possibility that consumers are not concerned about security and privacy when using new technology in some settings. The following hypothesis was developed in light of the requirements for high-security measures in FinTech and the expectations set forth in the qualitative phase of the study:

H7: 

There is a significant relationship between security and privacy and consumers’ intention to use FinTech services.

5.3.2. System Quality

In both the original DeLone and McLean Information Systems Success Model (D&M model) [98] and its amended version [99], system quality is put forth as one of the most strategic elements. It is essential for obtaining the production output associated with an information processing system. The technological level of a system’s information production success determines its quality [98]. This pertains to the software and data elements and measures the system’s technical soundness [100]. Customer impressions of information retrieval and service delivery are key indicators of system quality [101]. Users may require certain features of system quality, such as usability, availability, dependability, adaptability, accessibility, and response time [99]. The body of research highlights the significance of system quality as a key indicator of the desire to utilise a variety of technologies [102,103,104,105,106]. This implies that higher system quality encourages more consumers to use the services. According to the expectations of the informants described in the qualitative study and the aforementioned literature, FinTech necessitates a thorough system structural design, a quick response time, and reliability to perform various operations in a flexible process without interruptions. As such, the hypothesis below was developed:

H8: 

There is a significant relationship between system quality and consumers’ intention to use FinTech services.

5.3.3. Organisational Attributes

Firm Reputation

One of a company’s most important intangible assets is “reputation” which is built through time through the establishment of its credibility and believability [107] and of which primarily influences the behaviour of its clients [108]. Based on previous behaviour and anticipated future outcomes, an organisation’s corporate reputation determines whether it will be preferred by stakeholders over its main competitors [109]. Consumer decision-making has long placed a premium on corporate reputation [107]. When creating their overall perceptions, consumers give a lot of weight to a company’s reputation [110,111,112,113,114]. Many studies have shown the importance of reputation in predicting technological adoption [115,116,117]. This means that customer behaviour regarding a company’s services is formed based on the company’s honesty, goodwill, and capacity to provide efficient, advantageous, and trustworthy services. Nevertheless, little attention has been paid to how a company’s reputation may affect FinTech usage. In line with the expectations stated in the qualitative investigation and the existing literature, this study hence hypothesised that:

H9: 

There is a significant relationship between a firm reputation and consumers’ intention to use FinTech services.

5.4. Environmental Attributes

Government Support

The government has the ability to improve service credibility and reliability by promoting the application of technology in financial innovations and making infrastructural investments. Governmental provision drives consumers’ sense of security when utilising financial services [118]. A strong correlation between government funding and consumers’ adoption of innovation has been discovered via empirical studies [118,119,120,121]. However, several studies also claim the non-significance of governmental support as a factor in this context [122,123,124]. This suggests that the level of governmental support varies by country and environment. There is, however, a scarcity of research on the connection between governmental support and consumers’ intention to use FinTech services in the UAE. Based on all the above, this study hence hypothesised that:

H10: 

There is a significant relationship between governmental support and consumers’ intention to use FinTech services.

6. Phase Two: Quantitative Data Analysis and Results

6.1. Sample of Study

In order to identify any missing values, descriptive statistics analysis was used with IBM-SPSS; the findings revealed none. However, out of the 338 cases, six were identified to be multivariate outliers and therefore removed [125]. This process was performed using the conventional method for detecting multivariate outliers, which involves computing the squared Mahalanobis distance at p < 0.001 for each case in the dataset [126]. Ultimately, for the actual data analysis, 332 cases were used.

Table 3. Sample’s Demographic Description N = 332 [100%].

	Grouping	Frequency	Ratio
Gender	Male	217	65.4%
	Female	115	34.6%
Age	18 to 39 yrs	209	63%
	40 yrs and above	123	37%
Education level	College Diploma	28	8.4%
	First Degree (Bachelor)	130	39.2%
	Professional certificate	62	18.7%
	Others	5	1.5%
Occupation	Professional, e.g., lawyer, doctor, engineer	148	44.6%
	Manager/Executive	38	11.4%
	Academician	34	10.2%
	Student	28	8.4%
	Merchant/Businessman	77	23.2%
	Unemployed	4	1.2%
	Other	3	0.9%

summarises the demographic statistics. Harman’s single-factor test was used to detect common method bias typically occurring in survey data. A value of 37% was attained, and as it was lower than the threshold value of 50%, it was concluded that common method bias did not occur in the data [127].

6.2. Assessment of Measurement Model

The measurement model assessment entails the evaluation of reliability (composite reliability (CR)), convergent validity (factor loadings and average variance extracted or AVE), and discriminant validity (Hetrotrait-Monotrait (HTMT)) [42].

Table 4. Summary Results of Convergent Validity and Reliability.

Constructs	Indicators	Loadings	CR	AVE
Intention	IN1	0.819	0.966	0.802
	IN2	0.828
	IN3	0.942
	IN4	0.932
	IN5	0.939
	IN6	0.927
Performance Expectancy	PE1	0.830	0.897	0.687
	PE2	0.906
	PE3	0.817
	PE4	0.755
Effort Expectancy	EE1	0.869	0.884	0.658
	EE2	0.743
	EE3	0.753
	EE4	0.871
Facilitating Conditions	FC1	0.742	0.895	0.632
	FC2	0.842
	FC3	0.833
	FC4	0.840
	FC5	0.705
Social Influence	SI1	0.834	0.900	0.693
	SI2	0.895
	SI3	0.882
	SI4	0.705
Personal Innovativeness	PI1	0.844	0.888	0.665
	PI2	0.734
	PI3	0.801
	PI4	0.876
Customer Awareness	AW1	0.852	0.924	0.710
	AW2	0.787
	AW3	0.896
	AW4	0.878
	AW5	0.793
System Quality	SQ1	0.720	0.902	0.609
	SQ2	0.877
	SQ3	0.802
	SQ4	0.824
	SQ5	0.849
	SQ6	0.568
Firm Reputation	FR1	0.905	0.934	0.738
	FR2	0.837
	FR3	0.865
	FR4	0.812
	FR5	0.873
Security and Privacy	SP1	0.884	0.905	0.704
	SP2	0.850
	SP3	0.828
	SP4	0.790
Government Support	GS1	0.857	0.951	0.794
	GS2	0.858
	GS3	0.924
	GS4	0.925
	GS5	0.889

All factor loadings are significant at p < 0.05.

presents the results for the factor loadings, AVE, and CR, namely 0.70, 0.5, and 0.7, correspondingly [42,43,44,128].

HTMT ratios were used to assess discriminant validity.

Table 5. Heterotrait-Monotrait Ratio (HTMT).

	AW	FR	EE	FC	GS	IN	PE	PI	SI	SQ	SP
AW
FR	0.377
EE	0.505	0.394
FC	0.551	0.619	0.729
GS	0.203	0.624	0.323	0.558
IN	0.680	0.367	0.567	0.651	0.305
PE	0.578	0.451	0.694	0.652	0.298	0.847
PI	0.785	0.473	0.385	0.486	0.323	0.600	0.475
SI	0.352	0.614	0.568	0.753	0.530	0.493	0.524	0.317
SQ	0.611	0.648	0.641	0.784	0.523	0.678	0.708	0.471	0.651
SP	0.557	0.574	0.486	0.656	0.372	0.596	0.643	0.419	0.571	0.843

presents the results, i.e., HTMT values of 0.85 and below for all the constructs [129]. This means that there are no discriminant validity issues in the dataset, thus confirming the validity of the measurement model.

6.3. Assessment of Structural Model

Although discriminant validity had been identified in the evaluation of the outer model, lateral collinearity problems could result in statistical instability and/or unreliable conclusions [130]. This issue was hence examined. VIF ≥ 5 suggests possible collinearity problems [43,44,128,131]. As shown in

Table 6. Structural Model Results.

H	Relationship	Std.β	t- Statistics	p-Values	Confidence Intervals		Decision	VIF	[ƒ²]
					Lower	Upper
H1	PE » IN	0.496	12.446	0.000 ***	0.413	0.571	Supported	2.132	0.420
H2	EE » IN	−0.002	0.096	0.923	−0.092	0.096	Not supported	2.265	0.003
H3	SI » IN	0.043	1.038	0.299	−0.033	0.116	Not supported	2.000	0000
H4	FC » IN	0.122	2.713	0.007 **	0.035	0.208	Supported	2.763	0.014
H5	Aw » IN	0.259	5.515	0.000 ***	0.164	0.348	Supported	3.027	0.082
H6	PI » IN	0.155	2.709	0.007 **	0.037	0.263	Supported	2.077	0.035
H7	SP » IN	0.070	1.433	0.152	−0.028	0.173	Not supported	2.737	0.003
H8	SQ » IN	0.169	2.685	0.007 **	0.042	0.291	Supported	3.499	0.028
H9	FR » IN	0.158	3.472	0.001 **	0.248	0.074	Supported	2.068	0.043
H10	GS » IN	0.029	0.782	0.434	−0.052	0.102	Not supported	1.690	000

** p < 0.01, *** p < 0.001.

, the results support the assertion that multi-collinearity had no impact on the outcomes because all values VIF < 5.

Next, the structural model assessment was conducted for hypothesis testing. Bootstrapping was conducted using 5000 iterations following the suggestion of Hair et al. [128]. The results are presented in Table 6 and Figure 1. An R² value of 72.2% was generated, explaining the substantial variance in behavioural intention, fulfilling the previously set [43,44,128,131]. Additionally, determined was the effect size [ƒ²], whereby a majority of the variables were found to have a small to medium effect based on Cohen’s criteria [132]. The result of the predictive relevance (Q²) for the endogenous construct is larger than the null (Intention: Q² = 0.579) [128].

This research also examined the PLS predict as a validation of the model’s predictive relevance. PLS predict was employed to calculate the case-level predictions of PLS predict whereby k = 10, following the suggestions of Hair et al. [42] and Shmueli [43]. The Q² predict values were shown to be above zero (Q² predict > 0), suggesting that all the indicators had outperformed the Linear Model (LM) benchmark. This allowed for the RMSE values and naïve LM benchmark to be compared. The PLS-SEM and LM results’ comparison showed that indicators generated smaller prediction errors in comparison to the LM (PLS-SEM < LM) (see

Table 7. Results of PLS Predict.

Indicators	PLS Predict		LM predict		[LM-PLS]
	RMSE	Q² Predict	RMSE	Q² Predict	RMSE
IN4	0.511	0.634	0.498	0.653	−0.013
IN3	0.537	0.613	0.565	0.572	0.028
IN6	0.543	0.602	0.533	0.617	−0.01
IN2	0.628	0.417	0.586	0.494	−0.042
IN1	0.685	0.459	0.720	0.403	0.035
IN5	0.540	0.635	0.575	0.586	0.035

Intention = IN; Root Mean Squared Error = RMSE; Linear Model = LM.

), suggesting the model’s medium predictive power [43]. This means that the model can predict responses accurately utilising out-of-sample data and generating testable predictions.

7. Discussion and Conclusions

7.1. Meta-Inference

Meta-inference was used in conjunction with the bridge technique to reconcile the qualitative and quantitative data in order to evaluate the study’s conclusions [133,134]. According to the qualitative data study, consumer awareness and individual inventiveness are important personal elements that might accelerate the acceptance of FinTech services. The qualitative findings also suggested that the technical aspects of FinTech might operate as impediments, whereas system quality, security, and privacy may have the greatest bearing. The informants also emphasised that the organisational, motivational forces or barriers for consumers were constrained by their value judgements of the features of firms (i.e., reputation). Additionally, it was determined that important environmental constraints impacting consumers’ propensity to use FinTech services included government-related policies, regulations, and incentives. The quantitative data analysis mostly supported the preliminary qualitative findings, which revealed that consumer awareness, innovation, system quality, and reputation of FinTech providers have a substantial influence on consumers’ inclination to use FinTech services.

Meanwhile, the effects of governmental support, as well as security and privacy, were negligible. Accordingly, the majority of the qualitative findings could be generalised using quantitative research according to the study’s findings, which suggests that the mixed-method approach successfully bridges the qualitative and quantitative research gaps and synchronises the advantages of both research methodologies. Cross-referencing the empirical results in both research methodologies is beneficial to deepen comprehension of the specific research issue.

7.2. Discussion

This study shows that the usefulness of FinTech services in managing finances, ensuring efficiency, and saving time (i.e., performance expectancy), together with the accessibility of the necessary technical resources for users (i.e., facilitating conditions), could significantly increase consumers’ intention to use FinTech services. The underlying cause behind such a result is the novelty of such service in the culture of the UAE, where most errands are preferred to be served in a traditional manner from physical locations. These findings are consistent with the UTAUT paradigm and earlier research [23,52,58,59]. Effort expectancy was insignificant, in contrast to the UTAUT model’s assertion. This means that clients do not typically evaluate the significance of FinTech services based on their practicality, simplicity of use, interactivity, or competence. This behaviour may be attributed to reluctance and a lack of creativity when trying new services. These results may also be linked to different societal characteristics and principles influencing people’s perceptions [135]. Likewise, this study found the negligible impact of social influence. This might be explained by the users’ perception of financial concerns as a solitary and private activity, which justifies their sparse information sharing with peers and lessens the effect of peer pressure. Another reason for this finding is that the majority of the sampled respondents were young adults (18 to 39 years old), i.e., members of Gen Y who were born and raised in the technological era. This generation differs from Gen X in that it is more “self-directed” [70]. These conclusions suggest that the UTAUT model is relevant for explaining consumers’ use of FinTech services in the UAE.

This study’s two investigation phases demonstrated that consumer knowledge strongly influences consumers’ intention to use FinTech services. This indicates that empowered individuals may find them practical for managing their financial tasks. The use intention of a consumer would be positively influenced by prior information and educated curiosity regarding the existence, objectives, and numerous benefits of FinTech. This outcome is in line with past studies [81,82,83,136]. This result signifies that people are likely to be aware and find it meaningful to use FinTech services as it would benefit them in managing their financial tasks efficiently.

The qualitative and quantitative findings suggest that clients with higher degrees of inventiveness should have more favourable attitudes about FinTech services with a significant predictive relevance and substantial effect size. Hence, personal innovativeness is a significant barrier for consumers to use FinTech services, leading to poor uptake. These findings suggest that less innovative users may not prefer the new services as they are a relatively creative and advanced approach that is technologically different from other traditional banking methods. The reason for such behaviour might be the lack of understanding about the services, lack of an innovation mindset, uncertainty about the technology itself, fear of failing, and the time and effort that has to be spent to understand and master those innovative services. This conclusion backs the findings of previous research [70,90].

Security and privacy, according to bank specialists, are crucial for increasing consumers’ intentions to use FinTech services. The consumers did not, however, seem to share this impression throughout the quantitative phase, indicating that security and privacy issues did not rank highly with UAE consumers. The consumers’ upbeat opinions might be linked to the strict secrecy regulations and the reliable framework of the UAE’s financial sector, which is one of the most renowned and well-established in the world. Additionally, the idea of desensitisation, whereby society is used to living and working in a vulnerable environment, may be connected to decreased customer anxiety about impending security dangers and privacy violations. The consumers, on the other hand, were apprehensive about system quality. It became clear that they are drawn to a system’s capabilities and dependability. This is in line with the assertion of existing literature, which showed that when a technology system’s quality is improved, customer perception is significantly affected [102,103,104,105]. In both study phases, it was also shown that a company’s reputation has a substantial influence on consumers’ intentions to use FinTech. This suggests that having a high reputation indicates that a company offers dependable services due to its integrity and goodwill. Consumers today greatly rely on an organisation’s reputation in the market because there are more possible negative implications of choosing the wrong service providers. Customer intent is, therefore, driven mainly by a company’s prestige in the marketplace. This backs up the argument made in the literature that corporate reputation is an intangible organisational driver of technology adoption [116,117,137].

Furthermore, the bank professionals revealed throughout the qualitative phase that governmental support is an environmental factor which improves clients’ inclination to use FinTech services. The consumers, however, did not believe that the UAE government’s involvement had changed their intentions. This could be due to the fact that the governmental measures have not been successful or that the users believe that the measures have no effect on their decision to utilise such services.

7.3. Theoretical Implication

The empirical results of this study significantly advance academic knowledge on how consumers use FinTech services. Firstly, the study looked at six new elements that could explain the difficulties faced by clients in the UAE when using FinTech services. Secondly, the study increased the UTAUT model’s relevance and reliability in justifying the uptake of FinTech services in the UAE. Third, the study provided an overview of the important UTAUT factors influencing Emirati consumers’ acceptance of FinTech services. The model’s ability to explain a large amount of variance demonstrates the UTAUT framework’s applicability and effectiveness to this study. It is noteworthy that the main contributions not only replicate the UTAUT model in a new environment but also considerably advance the theory by integrating six new critical components. The PLS predict analysis demonstrated that the study’s model had medium predictive power as a broader theoretical contribution. This suggests that the model may produce testable predictions and reliably forecast reactions from beyond the sample.

Through the use of two complimentary analytical techniques, TCA and PLS-SEM, this research adds to the body of existing FinTech literature. By putting out six fresh combinations of obstacles preventing the use of FinTech services, TCA made a significant contribution to the findings. Additionally, the PLS-SEM results demonstrated the overall impacts of the newly added factors and UTAUT variables on the uptake of FinTech services. The subsequent findings supported the hypothesis that some variables, which were unimportant in the PLS-SEM analysis, could encourage the use of FinTech when combined with other variables.

7.4. Practical Implications

The study’s findings can assist scholars and policymakers in better understanding the effects of FinTech. The study can serve as a useful resource in creating effective policies that aim to maximise the advantages for mass consumers, service providers, and the national economy. The study explored and discussed how various individual, technological, organisational, and environmental attributes affect the intention of consumers to use FinTech services. The study offers a model for practitioners by better describing the actual difficulties that their clients face when utilising FinTech services. In order to ensure a smooth transition to digital consumer behaviour, it offers a robust framework for policy design and the planning and coordination of development strategies.

According to the study results, FinTech service providers in the UAE should focus less on social influence and more on the unique characteristics of their consumers, such as awareness and encourage their innovativeness to boost their perceptions via direct marketing. In order to maintain consistency in quality, FinTech providers should focus on providing helpful, accessible, quick, convenient, functional, and flexible services instead of focusing on security and privacy precautions, which are the areas that consumers in the UAE care about the least. Additionally, a company should maintain a strong reputation in the marketplace instead of seeking governmental backing because this intangible asset is essential for luring consumers to use FinTech services.

As practical incentives to stimulate the use of FinTech services, potential users would be offered practical financial services delivery channels in the shape of pleasant and quick service quality combined with strong security and reliability at more affordable costs. FinTech embracing will also enable FinTech companies to lower high operational expenses and avoid the waste associated with traditional processes. Economic improvements, digitalisation, social advantages, and sustainability goals are other incentives for using FinTech.

7.5. Limitations and Future Directions

To give the necessary insight for particular scenarios, this study primarily investigated the idea of FinTech services from a demand viewpoint (consumers). However, because one study cannot address all of these difficulties at once, it did not test the idea from the supply side. Hence, this study suggests that future research looks into the obstacles to FinTech adoption from the standpoint of FinTech providers. A deeper understanding of consumer behaviour could also be attained by including the moderating role of demographic parameters such as gender and age. Re-investigating the study’s research model in diverse industrial segments such as hospitality, healthcare, or education, and in other contexts such as the Gulf Cooperation Council (GCC) or the Developing-8 (D-8) nations which have similar development indicators to the UAE’s, could help to further validate the conclusions of this study. The study is limited to a geographically specific sample, i.e., the UAE. A noteworthy suggestion for future directions is to conduct a comparative study that compares the results obtained in this study with similar studies conducted, for example, in the EU, the USA, or other parts of the world.