Determinants of Behavioral Intentions to Use Islamic Financial Technology: An Empirical Assessment

Abstract

This study examines the antecedents/determinants of behavioral intentions toward the utilization of Islamic financial technology for Middle Eastern customers. The study applied structural equation modeling (PLS-SEM). After robust research efforts were invested in the identification of factors, they and were converted into measures, and the results were analyzed. The results demonstrate that the independent variables shown in the UTAUT model have a significant impact on the behavior to adopt Islamic financial technology, which implies that the people are ready to use Islamic financial technology while making online transactions. The work in this study adds to the knowledge regarding the factors affecting behavioral intention to use Islamic fintech, as there is scarcity of studies in this domain, especially in the context of Middle Eastern online customers. Moreover, this study also considers the major categories of online payments.

1. Introduction

The field of Islamic finance is growing and gaining interest globally, irrespective of Islam followers, and it is appreciated even by non-Muslims. Islamic finance is the form of finance which uses sharia principles of ethics and morality at its core in its financial dealing (Dharani et al. 2022; Rabbani et al. 2021b; Naeem et al. 2021). There was always a possibility that financial technology could contribute significantly to the development of Islamic finance (Banna et al. 2021b). In recent times, the technology has become an inseparable part of life, and it is impossible to imagine life without technology (Banna et al. 2021c). The information and communications technology in transforming the economic and social environment in many developing countries and fintech has a significant role to play in reshaping the global finance world (Abbasi et al. 2021).

Many researchers have defined financial technology as a tool of digital technology used for performing financial activities (Saksonova and Kuzmina-Merlino 2017; Mohamed and Ali 2018). Financial technology plays a significant part by filling in as a trusted financial middleman facilitating financial transactions for people and helping them in their daily activities globally, which has totally transformed initiates into a new era in the financial services market and proves to be an effective tool for banks and other financial institutions (Chinnasamy et al. 2021; Nguyen et al. 2021; Baber 2020a). Financial technologies and its tools have transformed the ways of performing financial activities (Todorof 2018). The rising investment in the field of financial technologies globally is a strong and reliable indicator of the growth in financial technologies. In this regard, the statistics published show that the transaction value will reach USD 8,971,956 million by 2022, at a growth rate of 17% per annum (Standard 2021).

Islamic fintech is defined in the same way as its conventional counterparts, with a difference of sharia compliance (Rabbani et al. 2020; Chong 2021). It is a combination of two words, Islamic + fintech. While the former refers to sharia compliance of financial transactions, the latter refers to the use of technology in the delivery of financial services. If any transaction performed under Islamic finance uses technology without complying to sharia principles, then it is considered as Haram or invalid (Lajis 2019; Hassan et al. 2020). Islamic finance has already embraced fintech based on technological advancements such as blockchain (Karim et al. 2022), artificial intelligence (Khan et al. 2021), cryptocurrency (Khan and Rabbani 2020); Regtech (Rabbani et al. 2021b), crowdfunding and P2P lending (Mohd Nor et al. 2021; Ascarya 2021). Briefly, fintech is about performing financial transactions with the use of disruptive, innovative and pioneering technology, whereas Islamic fintech is about doing all that within the sharia universe (Karim et al. 2022; Khan et al. 2021). Since Islamic finance is already a growing market and Muslims around the globe are looking for sharia-compliant financial solutions (Khan and Rabbani 2020; Rabbani et al. 2021a), fintech can help the Islamic finance industry in further growth by increasing accessibility, efficiency and transparency (Banna et al. 2021a).

The increasing use and growth of technology around financial services can be attributed to many reasons such as reducing opportunity cost and encouraging higher customer satisfaction, which results in increased bank efficiency, as people can use financial services anytime and anywhere with only a click of a button if they are connected to the internet (Rabbani 2022; Tajudin et al. 2020). Islamic fintech is critical to digital delivery of Islamic financial services to customers to fulfill the Islamic finance objectives of financial inclusion, social justice, and equal distribution of wealth within a society (Baber 2020a). Islamic social finance services such as Zakat, Qardh-Al-Hasan, and Islamic microfinance can be delivered using artificial intelligence and blockchain technology to achieve this objective (Atif et al. 2021; Rabbani 2021; Khan et al. 2021).

There is much research such as (Yan et al. 2021; Ashta and Biot-Paquerot 2018; Lee and Shin 2018; Hu et al. 2019; Mazambani and Mutambara 2020; Ahmed et al. 2020; Fu and Mishra 2022) that emphasizes the merits of financial technology. As they explain in their studies, financial technology helps to increase transparency, accessibility, and flexibility, and it is less risky and improves shareholders earnings. Second, the growth in financial technology can also be attributed to the fact that many people are connected to mobile internet services. In this regard, global systems for mobile communication associations have predicted that by 2025 mobile internet users will be more than 5 billion. All these factors imply that the financial technology market will expand widely and quickly in the next few years (Hendratmi et al. 2019; Chinnasamy et al. 2021; Baber 2020a).

Based on several arguments and explanations, it can be safely concluded that Dubai has great potential to improve its economic growth by facilitating the role of financial technology. Although best efforts have been placed in reviewing the literature, it can be understood that many previous studies have focused on different variables such as consumer behavior, some of which observe consumer intention while studying intentions to use Islamic financial technology, especially in the Middle East.

Although financial technology has also attracted shareholders, the long-term use of financial technology still has doubts. Much research is doubtful due to unexpected and considerable risks that hinder the facilitation of financial technology. Therefore, the present study uses TAM and UTAUT1 (Buckley and Webster 2016). These theories have been used while investigating customer intentions.

The remainder of the paper is structured as follows: the literature review section presents the relevant theories used in this paper. In the next section, the conceptual framework is presented, and a hypothesis was developed. Further, in the research methodology section, the research methods and variables are discussed. In the data analysis section, empirical results of this study are presented and discussed and in the last section of the study, which summarizes and highlights the main findings and contributions of this study to the existing body of knowledge.

2. Review of the Literature and Hypothesis Development

2.1. Theory of Planned Behavior (TPB)

The intention to use any technology was researched in light of the theory of planned behavior. The theory of planned behavior is popular, as it is an efficient model to clarify intentions and predict behavioral intentions for using any technology (Hu et al. 2019). The theory of planned behavior is also an extension model from the theory of reasoned action (Mittelman and Rojas-Méndez 2018). The theory of planned behavior advocates that the tendency of people performing certain activities increases if they find that that behavior will yield the intended results or if they believe that some valued person wants them to behave in a certain way or if they assume that they have ample resources and opportunities to indulge in such kinds of behavior (Zaremohzzabieh et al. 2019; Bhatti and Md Husin 2019).

2.2. Technology Acceptance Models

The technology acceptance model was initially introduced by Davis (1986) and has been constantly revised and upgraded to the technology acceptance model (TAM) 2 (Venkatesh and Davis 2000) and then TAM3 (Venkatesh and Bala 2008).

The technology acceptance model (TAM) is an important model to produce system use (Maranguni and Grani 2015; Chau 1996). Many researchers have suggested that TAM can be combined with other theories and models, and they believe that the technology acceptance model is an important and effective model in determining the use of growing technology within an organization. Janah et al. (2020) suggested potential factors from the technology acceptance model that influence customer intentions to adopt financial technology products and services in Malaysia.

The study concluded that perceived ease of use and perceived usefulness have a positive effect on behavioral intentions to adopt financial technology production services. However, perceived risk and cost behave on the contrary. Acceptance is a variable adapted from the technology acceptance model 3. It also states that behavioral intentions are designed by two beliefs, namely, perceived benefits and perceived ease of use.

Technology acceptance model 3 came into existence when technology acceptance model 2 was combined with the perceived ease of use determinant (Venkatesh and Bala 2008; Venkatesh and Davis 2000). Technology acceptance model 3 is the latest model in the area of technology acceptance and includes SNS images, JR, output quality, computer self-efficacy, and other factors that suggest computer anxiety and that effect perceived ease of use and perceived usefulness to determine consumer behavioral intentions.

2.3. Use of Technology

The UTAUT model was introduced and designed 20 years ago by Venkatesh and Davis (2000) from the eight latest accepted technology models. The UTAUT model developed important factors that highlight the importance of using technology primarily in an organization context. The four main contributions of this model are perceived enjoyment, effort expectancy, social influence and facilitating conditions that are intended to effect intentions to use technology (Venkatesh and Davis 2000).

The model was further developed into the UTAUT 2 model, and three more constructs were added: price value, Hedonic motivation, and habits (Venkatesh et al. 2012). Ashta and Biot-Paquerot (2018) and other researchers have shown that in the context of customer adoption of technology, the effects of Hedonic motivation, price value, and habit are significant and complex.

2.4. Conceptual Framework and Development of Hypothesis

The influence of behavioral intention in the context of financial technology is the area of concern in marketing management research. However, there is a paucity of research regarding Islamic financial technology. Therefore, in this study, behavioral intention is considered as a dependent variable, and the variables of the use of a technology model are considered as independent variables. This relationship between the use of Islamic financial technology and behavioral intention is observed in several studies (Solarz and Swacha-Lech 2021; Baber 2020b; Suryanto and Dai 2020; Kou et al. 2021; Banna et al. 2021b; Abbasi et al. 2021). The study concluded that habits strongly influence customer intention to use mobile banking. In another study, Thaker et al. (2020) ascertained that several components of the UTAUT model were significant in shaping the behavior intentions of customers. Therefore, the hypotheses were framed in light of the literature as depicted in the hypothesized model in Figure 1 below.

It is well presented in the literature that performance expectancy influences the intention to use financial technology tools (Akhtar et al. 2019).

Hypothesis 1.

Performance expectancy (PE) has a direct and positive relationship with behavioral intention to use (BI) Islamic fintech.

Similar to the study by Thaker et al. (2019), it was established that effort expectancy also influences the intentions of customers to use financial technology.

Hypothesis 2.

Effort expectancy (EE) has a direct and positive relationship with behavioral intention to use (BI) Islamic fintech.

This study also considers SI, FC, and HA, while considering the use of technology. Raza et al. (2019) investigated the factors that facilitate mobile banking acceptance of Islamic banks in Pakistan. The study used the UTAUT2 model with all indicators as an independent variable and behavioral intention as a dependent variable. In their study, it was concluded that habits significantly influence customer behavioral intention to use mobile banking with Malaysian Islamic banks. Baptista and Oliveira (2015) also conducted a similar study in Africa using the UTAUT model and measures of behavioral intentions.

Hypothesis 3.

Social Influences (SI) have a direct and positive relationship with behavioral intention to use (BI) Islamic fintech.

Hypothesis 4.

Facilitating conditions (FC) have a direct and positive relationship with behavioral intention to use (BI) Islamic fintech.

Hypothesis 5.

Habits (HA) have a direct and positive relationship with behavioral intention to use (BI) Islamic fintech.

There are many research studies highlighting the influence of HM and PI on the intention to use financial technology (Haider et al. 2018).

Hypothesis 6.

Hedonic motivation (HM) has a direct and positive relationship with behavioral intention to use (BI) Islamic fintech.

Hypothesis 7.

Personal innovativeness (PI) has a direct and positive relationship with behavioral intention to use (BI) Islamic fintech.

3. Research Methodology

This study utilizes the mail methodology for the investigation of the measures of adoption of Islamic financial technology and behavioral intention of customers. The questionnaire was designed based on a theoretical literature review where the items were measured on a 5-point Likert’s scale from strongly disagree (5) to strongly agree (1).

The items of a questionnaire were thoroughly reviewed, and deliberations were made such that each item would develop according to the operational context of research variables. The questionnaire was divided into two main parts. The first part consists of the respondent’s demographics, and the second part tries to collect data on all the measures of the UTAUT model in the context of Islamic financial technology. A total of 24 questions were asked to measure the intentions of customers regarding the adoption of Islamic financial technology.

The research scale was assessed for its reliability, validity, and one-dimensionality before being utilized for the structural model. It was also assured by asking the respondents that they have a good and secure internet connection for using Islamic financial technology applications introduced by their banks.

The study focuses on the quality of respondents; therefore, the sampling criteria filter those people who can access Islamic financial technology services with a smartphone in various sectors and across many cities in Dubai (UAE). The finalized questionnaire was administered on a population of 1000 respondents from the period of October–January 2021.

In total, 721 research instruments were collected, and only 324 users of Islamic financial technology were identified and qualified for data analysis. From this record, 24 questionnaires were found to be incomplete and were discarded in the next stage. Finally, the study utilized 300 instruments for final data analysis.

PLS-SEM was utilized for data analysis, as it is a useful and flexible tool for the development of statistical models used in the searches in this area. Structural equation modeling can process a large sample, is proper for weak theoretical models, and does not require normality of data (Aguirre-Urreta and Ronkko 2015).

In another study, Hair et al. (2014) suggested the structural equation modeling method employed here as a superior method for small samples. Therefore, this study utilizes structural equation modeling through the use of lisrel 8.80. Initially, the measurement model was assessed, and a further structural model was finalized with all the variables being run in a single model.

4. Results and Discussion

4.1. Respondents Characteristics

The data show (Refer

Table 1. Demographics for all the respondents.

User Characteristics	Users (300)	%	Statistical Test
Gender
Male	205	69.1	F = 0.570
Female	95	30.9
Education background
Elementary school	10	2.7	F = 1.066
Junior high school	40	4.4
Senior high school	150	13.3
Diploma	20	6.6
Bachelor	80	30.3
Profession
Professional	77	25.8	F = 13.858
Student	78	26.6
Housewife	30	10.0
Businessperson	105	35.0
Others	10	3.3
Income
<3 million	106	35.3	F = 13.990
3–5 million	105	35.2
6–10 million	59	19.8
11–15 million	10	3.3
16–20 million	10	3.3
>20 million	10	3.3
Expenditure
<2 million	160	53.3	F = 9.009
2–4 million	100	33.3
5–9 million	20	6.66
10–14 million	20	6.66

) that 90% of customers use Islamic financial technology for the purpose of payments. Most of the respondents were males (63%). In terms of the level of education, 43.5% of the customers hold bachelor’s degrees. The data also show that most of the customers fall into the category of businesspersons (40%), professionals (27.2%), and students (23.8%).

4.2. Measurement Model Evaluation

The researchers working around PLS estimations explain both models simultaneously. The measurement model estimates and examines the accuracy of the model, whereas the second one, which is the structural model, investigates the relationships and defines the explanatory power of the model thus formed. The one-dimensionality was assessed though CFA (Confirmatory Factor Analysis). The values are given in Figure 2.

The figures (Refer

Table 2. Standard loadings, Cronbach’s Alpha, and t values.

S. No	Constructs	Standard Loadings	Cronbach’s Alpha	t Values
1.	Performance Expectancy	0.890	0.89	2.98
2.	Effort Expectancy	0.760	0.98	2.89
3.	Social Influence	0.887	0.78	3.87
4.	Facilitating Conditions	0.590	0.77	4.66
5.	Hedonic Motivation	0.578	0.78	4.77
6.	Habits	0.687	0.89	2.99
7.	Personal Innovativeness	0.890	0.90	1.98

) obtained for the assessment of the measurement model for composite reliability, standard loadings, t values and from the Alpha values from these statistics’ one-dimensionality, composite reliability and discriminant validity can be ascertained. In our study, all these statistical values were assessed. The study uses factor loadings, average variance extracted to support convergent validity and Cronbach’s Alpha to support reliability. Recommended values of standard loadings must be higher than 0.5 (Ryu 2018). Recommended Cronbach’s Alpha value to support reliability must be higher than 0.7 (Tenenhaus et al. 2005). The values were found to be in an acceptable range, as shown below, indicating one-dimensionality, reliability, and validity.

4.3. Construct Reliability (CR)

The internal consistency was ascertained through the calculation of construct reliability using the formula given below. The value should be greater than 0.6.

$$\boxed{CR=\frac{{\left[{\displaystyle \sum _{i=1}^{n}s{l}_i}\right]}^2}{{\left[{\displaystyle \sum _{i=1}^{n}s{l}_i}\right]}^2+{\displaystyle \sum _{i=1}^n{e}_i}}}$$

where sl = standardized loadings and e = corresponding error terms.

Variance Extracted (VE)

A supplementary measure was ascertained through the calculation of variance extracted using the formula given below. The value should be greater than 0.5

$$\boxed{VE=\frac{{\displaystyle \sum _{i=1}^{n}s{l}_i{}^2}}{{\displaystyle \sum _{i=1}^{n}s{l}_i{}^2}+{\displaystyle \sum _{i=1}^n{e}_i{}^{}}}}$$

All the values are in acceptable ranges. The

Table 3. Scale reliability of the study scales.

Construct	Construct Reliability	Variance Extracted	VIF
Performance Expectancy	0.79	0.46	3.44
Effort Expectancy	0.96	0.80	4.34
Social Influence	0.89	0.67	2.90
Facilitating Conditions	0.85	0.55	3.77
Hedonic Motivation	0.85	0.54	4.77
Habits	0.89	0.63	2.97
Personal Innovativeness	0.88	0.61	3.00

The VIF (Variance Inflation Factor) score is greater than the acceptable range, i.e., >2.5 ensuring multicollinearity.

below depicts CR and VE values for all study scales.

4.4. Structural Model Evaluation

After the measurement model is assessed, the next step is the assessment of the structural model for hypothesis testing and generation of a final model from which the results can be interpreted. The hypothesized structural relationships were assessed between measures of the UTAUT model and the behavioral intention of customers to adopt Islamic financial technology. The assessment was carried out based on statistical significance of the path values which were obtained after the model was converged.

The results in Figure 3 and

Table 4. Path estimates of all research relationships.

Relationship	Path Estimates
PE-------BI	0.63
EE-------BI	0.11
SI-------BI	−1.04
FC-------BI	1.12
HA-------BI	−0.34
HM------BI	0.98
PI-------BI	2.02

show that most of the path values were positive and directly affect behavioral intention to adopt Islamic financial technology by the customers, except for effort expectancy with a negative value of b = −0.11 (p < 0.05), social influence with a negative value of b = −1.04 (p < 0.05), habit with a negative value of b = −0.34 (p < 0.05), and hedonic motivation with a negative value of b = −0.98 (p < 0.05). The factors that affect behavioral intention positively are performance expectancy with a value of b = 0.063 (p < 0.05), facilitating conditions with a value of b = 1.12 (p < 0.05), and personal innovativeness with a value of b = 2.02 (p < 0.05).

5. Conclusions

The conclusion of the study should be in line with the objective, which was framed after the comprehensive literature review. The objective of this study was to provide a new and improved understanding of the influencing factors for behavioral intentions of customers for Islamic financial technology; in this regard, seven hypotheses were tested.

The findings of this study corroborate with previous several studies, such as (Agarwal and Zhang 2020; Choi et al. 2019; Ernst & Young 2019; Xie et al. 2021; Noreen et al. 2022; Singh et al. 2020; Dadoukis et al. 2021; Hu et al. 2019; Fu and Mishra 2022; Solarz and Swacha-Lech 2021), and concludes that financial technology increases the flexibility to use financial services.

In one of the remarkable studies conducted by (Nangin et al. 2020; Singh et al. 2020), PEOU is the most influencing factor in the behavior intentions of customers using mobile banking. This corroborates with this study and can be attributed to the fact that increased security and user friendliness in reaching financial technology services facilitates higher intentions in individuals to utilize financial technology.

Knowledge also facilitates individual intentions for using financial technologies, and slowly and gradually, financial technology becomes part of daily life activities and enhances customer intentions to use financial technologies more and more. Another study (Thaker and Allah Pitchay 2018) concluded that the other factor of the UTAUT model that has a strong relation with individual intentions to use mobile banking services in Pakistan is social influence. This implies that in many developing countries, the social environment plays an important role. The social environment includes family, peer pressure, friendship and imitating celebrities, which all play an important role in shaping an individual’s point of view regarding the acceptance of technology in the context of financial technology.

The acceptance model is an important model when it comes to influencing customer intentions to adopt mobile banking and other financial services. The results generated by this study bring a comprehensive perspective for researchers, policymakers, and practitioners in banking and institutional management, such that new policies can be designed by taking into consideration the factors that greatly impact the facilitation of the adoption of financial technology in Islamic banks.

Financial technology stakeholders should also take note about the most influential items that influence customer intentions for each Islamic financial technology application. The policies that were framed and implemented in light of this study are proven to be more beneficial and appropriate to customer needs. The results can be useful for any financial security in any developing country looking for the facilitation of Islamic banking financial technology services.

The main issues that can be addressed by Islamic financial technology service providers are facilitation of the benefit of Islamic financial technology and Islamic financial technology should be made beneficial and convenient to use. The main factors through which Islamic financial technology can be taken forward are price comparison to value and recommendations by users of other financial technology applications.