Sustainable Development through Fintech: Understanding the Adoption of Buy Now Pay Later (BNPL) Applications by Generation Z in Saudi Arabia

Abstract

Sustainable development has emerged as a paramount priority globally, with the fintech services playing a crucial role in achieving these goals. Although previous research has explored consumer perceptions of novel fintech services, including Buy Now Pay Later (BNPL) applications, there remains a knowledge gap regarding the factors influencing adoption among young consumers. This study addresses this limitation by investigating the determinants of BNPL app usage among Saudi Arabian Generation Z consumers, integrating the Unified Theory of Acceptance and Use of Technology and DeLone and McLean’s Information Systems Success model. A survey of 380 BNPL app consumers from Generation Z was conducted, and their responses were analyzed using structural equation modeling. The findings reveal that performance expectancy, effort expectancy, and social influence positively impact the intention to continue using BNPL apps. Furthermore, system quality, service quality, and information quality exhibit significant correlations with satisfaction within the IS Success model. The study’s outcomes are expected to inform app developers in designing effective apps that engage digital-native consumers and provide policymakers with insights to enhance fintech services. This research contributes significantly to the existing body of knowledge on fintech adoption, intentions, and satisfaction.

1. Introduction

Sustainable development has been a universal goal that has grabbed the attention of countries around the world. According to the United Nations (UN), fintech is one of most important industries for facilitating the achievement of Sustainable Development Goals (SDGs) [1]. Hence, it is not surprising that in the 21st century, fintech payment services have experienced massive growth, offering consumers various opportunities and mechanisms to make payments for their purchases [2,3]. A recent fintech payment mechanism is the Buy Now Pay Later (BNPL) technique. BNPL enables consumers to make immediate purchases while deferring payment to a later specified timeframe [4]. The innovative BNPL fintech service allows customers to apply for and receive approval for short-term loans at the point-of-sale using BNPL apps. Once approved, the customer can purchase goods and services and pay off the loan through a series of installments. Unlike credit cards, BNPL does not require credit assessments, but determines eligibility using algorithms. BNPL has spending limits, structured installments, and interest-free transactions, with penalties only for missed payments, making it a popular payment option worldwide [4,5].

A recent report by Grand View Research documented that the BNPL market has steadily grown since its inception in 2020. The global market size for BNPL reached USD 1.64 billion in 2022 and is projected to maintain a compound annual growth rate (CAGR) of 26.1% between 2023 and 2030 [6]. The report also highlights that Saudi Arabia is experiencing rapid growth in BNPL markets due to government support for fintech services. Despite Saudi government efforts to support BNPL apps, it is crucial to recognize consumer intention to continue using these apps and their satisfaction with them for long-term success, especially among digital-native consumers like Generation Z.

Saudi Arabia has made significant strides in its fintech industry, with a focus on achieving its Vision 2030 goals by prioritizing fintech as a crucial sector in its agenda. Moreover, Saudi Arabia is considered to be a ‘youth’ country as about 36.7% of its total population comprises individuals aged between 15 and 34 years old [7]. Studies suggest that Generation Z (hereafter Gen Z), i.e., those born in the mid-1990s to mid-2000s [8], is rapidly becoming a crucial demographic that businesses and financial institutions must cater to when it comes to payment options. This generation, born and raised in a hyper-connected and digitally driven world, prioritizes speed over accuracy and values immediate and seamless payment experiences. As digital natives who are fluent in using technology, their social and communication interactions lean toward a digital format as well [9]. Various neologisms have emerged to describe Gen Z, including iGeneration, Net Generation, and Generation Next, among others [10]. Hence, understanding their perceptions and the factors that influence their continued use of these apps is essential for the country of Saudi Arabia to achieve its 2030 SDGs. The Fintech Saudi Annual Report underscores the importance of consumer demand in reaching the target of 525 fintech companies operating in the Kingdom by 2030 [11]. As Gen Z prioritizes immediate and seamless payment experiences, businesses and financial institutions need to tailor their fintech payment apps to meet this demographic’s preferences and expectations.

While the use of BNPL payments will continue to increase significantly in Saudi Arabia, there remains a considerable research gap in the study of Gen Z’s attitudes, behaviors, and adoption patterns regarding BNPL apps. This is increasingly important as Gen Z individuals are known for their high-consumption habits and quick-gratification lifestyle, making pay-later mechanisms a viable option [12]. The lack of studies on Gen Z’s adoption of and satisfaction with BNPL apps presents a challenge for businesses to meet their demands and preferences, despite their established recognition as a critical consumer demographic. Therefore, further research is needed to understand the intention to continue using BNPL apps among Gen Z consumers and their satisfaction with these payment apps in Saudi Arabia. This paper aims to fill this gap by examining the factors that influence the continued intention of BNPL apps among Gen Z consumers in Saudi Arabia, and to determine their overall satisfaction with these apps, using a novel merged model that combines the UTAUT model and DeLone and McLean’s IS Success model.

This study aims to address a critical knowledge gap in the existing literature on the adoption of BNPL apps among Gen Z consumers in Saudi Arabia. While previous studies have explored the factors influencing technology adoption (e.g., [13,14]), these investigations have often employed limited models and have been conducted in a narrow range of countries. In contrast, this study seeks to contribute to the existing body of knowledge by investigating the factors that influence Gen Z’s continued intention to use BNPL apps in Saudi Arabia, using a novel merged model that combines the UTAUT model and DeLone and McLean’s IS Success model. The lack of studies on BNPL use among Gen Z in Saudi Arabia, where investments in fintech are great, and young-generation population numbers are large, highlights the need for additional research. This research therefore seeks to fill the gap in understanding the factors influencing Gen Z’s adoption of BNPL apps in Saudi Arabia by providing insights into these factors, ultimately shedding light on the factors driving BNPL adoption among Gen Z in Saudi Arabia.

The remainder of this paper is structured as follows: Section 2 presents a review of the literature; Section 3 presents the theoretical bases and hypothesis development; Section 4 presents the research methodology; Section 5 describes the results; Section 6 presents the discussion and contributions; Section 7 describes the limitations and future research directions; Section 8 concludes the study.

2. Literature Review

The United Nations’ 17 Sustainable Development Goals (SDGs) aim to ensure prosperity, peace, and the elimination of poverty [15]. Fintech has been recognized as a key enabler of financial inclusion and SDG achievement, as it increases access to various financial services, reduces costs, and promotes equal opportunities [16,17]. Fintech services can empower financial inclusion, drive economic growth, and achieve sustainable development by increasing accessibility, reducing costs, and promoting equal opportunities [18].

The recent emergence of BNPL apps offers a new opportunity for fintech to facilitate financial inclusion. By providing consumers with greater flexibility and convenience, BNPL apps can expand access to financial services and contribute to SDG achievement. This is particularly important for individuals with limited income and access to traditional financing sources, such as Gen Z.

Similarly to any other innovative technology, consumer perceptions and satisfaction are crucial factors influencing the success of BNPL apps. While several studies have examined fintech and mobile payment adoption (e.g., [2,3]), the literature suggests that research on BNPL is in its early stages, with only a limited number of studies since its emergence in 2020, particularly on factors influencing consumer adoption and satisfaction. For example, Aalders [19] conducted a content analysis of three BNPL providers to identify responsible consumers: this study found that BNPL companies offer easier loans compared to credit card companies and define responsible consumers as those who make repayments on time. Furthermore, the redefinition of responsible consumers has resulted in increased consumption, particularly among lower-income families. In their literature review, Pattamatta and Dabadghao [20] highlighted that point-of-sale lending such as BNPL is commonly used by younger generations who are underserved and new to the credit environment. Feng et al. [21] agree that the BNPL mechanism attracts young adults without credit access. They developed a conceptual model to help small stores optimize their use of BNPL mechanisms. Johnson et al. [22] conducted a regulatory analysis of BNPL services in Australia. Through their content analysis, they identified failures in the regulations concerning consumer protection terms. Similarly, Tan [23] examined how BNPL corporations attempt to attract new and profitable consumers to Singapore’s debt industry. Their content analysis revealed that BNPL is perceived as a means to fulfill immediate consumption needs, potentially for unnecessary products, while concealing the fact that the consumers are entering the debt industry, especially young individuals without a credit profile.

The influence of BNPL on overconsumption or impulsive buying behavior has been an area of interest in several studies. For instance, Ah Fook and McNeill [24] examined the impact of BNPL on the impulsive buying behavior of young female adults in the online fashion context. Through a quantitative analysis using a questionnaire and impulsive buying behavior theory, they found that BNPL consumers tended to engage in higher levels of online consumption than non-BNPL consumers. They also identified a clear link between impulsive buying tendencies and pay-later tools, supporting the impulsive consumption concept. Similarly, Susanto et al. [25] explored the impact of BNPL and hedonic motivation on Gen Z’s impulsive buying behavior on Shopee, a popular e-commerce platform. They found that BNPL and hedonic motivation both positively affect Gen Z’s impulsive buying behavior on Shopee.

Very recent studies have applied theories related to technology adoption. Hidayat et al. [14] examined BNPL adoption in Indonesia using TAM and TPB theories. Their findings showed that perceived risk, trust, and subjective norms significantly influenced consumers’ usage intention, while perceived ease of use and perceived usefulness did not. In contrast, Jagadhita et al. [13] employed an extended TAM to investigate BNPL adoption in Indonesia, finding that perceived ease of use, perceived usefulness, and trust positively influenced continuance intention to use BNPL. While both studies confirmed the significance of trust, they differed in their findings regarding the impact of perceived ease of use and perceived usefulness. Notably, Jagadhita et al. [13] also found that trust mediates this relationship.

A few recent studies have considered demographic and generation specifications in adopting BNPL. For instance, Raj et al. [26] investigated the intention to adopt BNPL among undergraduate and postgraduate students with a sample consisting of Indian students. Using an extended TPB model, they found that subjective norms, attitude, and perceived behavior significantly influence intention to use BNPL, with trust playing a partial mediating role. Privacy concerns negatively impact trust and attitudes toward BNPL. Additionally, Behera and Dadra [27] employed the stimulus–organism–response (SOR) framework to investigate the attitudes of young consumers toward fintech credit services, including BNPL. Their study revealed that perceived usefulness, structural assurance, flexibility, and affordability exerted a significant influence on consumers’ attitudes toward fintech credit services. A further study was conducted by Juita et al. [28] in Indonesia that explored how gender differences affect the adoption of BNPL services. These researchers surveyed 257 consumers and found that digital financial literacy has a significant impact on the decision to adopt BNPL. Notably, their study found that financial literacy has a stronger influence on women’s decisions to use BNPL. Their study also discovered that both perceived financial and security risks are significant factors in consumers’ decisions, with financial risk having a greater impact. Schomburgk and Hoffmann [29] examined the effect of mindfulness on BNPL usage. Through a quantitative approach using a questionnaire in Australia, they discovered that mindfulness increased self-control and reduced impulsive buying behavior, thereby reducing the use of BNPL services.

This literature review reveals a scarcity of studies examining the factors driving consumer continuance intention to use BNPL apps, particularly among Gen Z. Existing research has sometimes yielded conflicting results, as seen in the studies by Jagadhita et al. [13] and Hidayat et al. [14], highlighting the need for further investigation. Moreover, the prevailing studies have employed limited technology adoption models (TAM, TPB, and SOR), which underscores the necessity to explore alternative frameworks and additional factors influencing BNPL adoption. Furthermore, the existing literature is confined to a narrow range of countries, including the UK, Bangladesh, Australia, Singapore, India, and Indonesia. Saudi Arabia, with its significant investments in fintech development, remains understudied in this area. Targeting this context will contribute to the existing body of knowledge.

3. Theoretical Bases and Hypothesis Development

3.1. Unified Theory of Acceptance and Use of Technology (UTAUT)

The UTAUT adoption theory, introduced by Venkatesh et al. [30], is built upon six existing models: the theory of planned behavior (TPB), the theory of reasoned action (TRA), the technology acceptance model (TAM), the motivation model (MM), social cognitive theory (SCT), and the diffusion of innovation (DOI). Recognizing the difficulty researchers faced in selecting among different theories and models, Venkatesh et al. [30] emphasized the need for a unified model to analyze consumer behavior in the realm of information technology.

The UTAUT model is commonly employed to explore consumer technology adoption. This model revolves around four key elements: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FCs). The term performance expectancy refers to the extent to which a person thinks that the system usage will enable them to improve their level of job performance. The degree of ease connected with the system usage is how effort expectation is defined. Social influence is the degree to which a person believes that significant individuals think they should use the new method. Facilitating conditions pertain to an individual’s confidence in the presence of adequate organizational and technical resources that support the utilization of the system. Venkatesh et al. [30] suggest that UTAUT explains up to 70% of variation in behavioral intention, which is significantly more than each of the six preceding theories and models, each of which had a maximum explanation of only approximately 40%.

The UTAUT model has been utilized by several studies in the technology adoption research generally (e.g., [31,32,33]) and in fintech and mobile payment adoption specifically (e.g., [34,35]). For instance, Alkhwaldi et al. [34] investigated behavioral intentions and loyalty in the aftermath of COVID-19 in a developing-country context to learn more about how consumers adopt fintech. The four basic UTAUT constructs, namely PE, EE, SI, and FCs, were combined with three additional elements, i.e., personal inventiveness, financial literacy, and uncertainty avoidance, to create a conceptual framework. The results showed that the components PE, SI, and FCs positively influence behavioral intentions. Moreover, factors affecting behavioral intentions to adopt fintech were examined by Antwi-Boampong et al. [36]. Their results demonstrated that PE and EE were predictive of Ghanaian fintech consumers’ behavioral intentions. In Indonesia, Mansyur and Ali [37] investigated the uptake of Sharia-compliant fintech among millennials. Their findings demonstrate that behavioral intention (BI) is considerably and positively influenced by PE, EE, and FCs. Furthermore, Chan et al. [35] examined consumer acceptance of fintech in Open Banking. Their research revealed that consumers’ intentions to use Open Banking are directly shaped by PE, EE, SI, and perceived risk. Rabaa’i [38] conducted an empirical analysis into Kuwait’s use of mobile wallets in the fintech industry. The findings of that study suggested that PE, EE, and FCs have an impact on consumers’ behavioral intentions to use mobile finance apps. By including personal innovativeness, trust, grievance, and anxiety as extensions to the UTAUT model, Patil et al. [39] investigated the adoption of mobile payment apps among Indian consumers. Their findings showed that PE and EE are important drivers of customer behavior while using mobile payments; also, SI and conducive circumstances have a big impact on individuals’ intentions to use them. Furthermore, Upadhyay et al. [40] extended UTAUT with perceived severity and self-efficacy to assess customer uptake of mobile payment services during COVID-19. The results showed that customers’ attitudes regarding the adoption of mobile payment services are significantly positively impacted by PE and EE. Additionally, this study examines actual users rather than non-users; so, it examines continuance intention to use BNPL apps. The examination of continuance intention has been accepted by several previous studies (e.g., [9,41,42,43]). In light of the aforementioned studies, the following hypotheses are proposed:

H1. 

PE has a positive impact on the intention to continue utilizing BNPL apps among Gen Z.

H2. 

EE has a positive impact on the intention to continue utilizing BNPL apps among Gen Z.

H3. 

SI has a positive impact on the intention to continue utilizing BNPL apps among Gen Z.

H4. 

FCs have a positive impact on the intention to continue utilizing BNPL apps among Gen Z.

In the technology acceptance model (TAM), the perceived ease of use (PEOU) is considered to have a positive effect on the perceived usefulness (PU) [44]. However, in the UTAUT model, EE encompasses the PEOU, while PE includes PU. Recent studies in the application adoption context have found that the PEOU directly predicts the PU (e.g., [45]). Therefore, the fifth hypothesis (H5) is proposed as follows:

H5. 

EE has a positive impact on the PE of BNPL apps.

3.2. Information Systems Success Model (D&M ISS Model)

The D&M ISS model introduced by DeLone and McLean [46] has become a commonly utilized framework for assessing the effectiveness of information systems (IS) across different research settings. The model encompasses six components, namely system quality (SQ), information quality (IQ), service quality (SRQ), satisfaction (SAT), intention, and usage behavior. DeLone and McLean’s [46] explanation of SQ involves it being a technical performance indicator that determines a service’s success. They added that SQ is usually evaluated based on its technical attributes, such as reliability, availability, adaptability, response time, and usefulness. SRQ has been defined as the assessment of services delivered by a provider of information systems. IQ encompasses completeness, customer acceptability, simplicity, and security of the content of an app. Numerous studies have examined the D&M ISS model in diverse contexts (e.g., [47,48]). Furthermore, a number of studies have examined the D&M ISS model in the fintech and mobile payment context (e.g., [49,50,51]). For instance, Purwati et al. [50] used the D&M ISS model to evaluate the satisfaction level and benefit of mobile banking usage in Indonesia. Their findings indicate that SQ, IQ, and SRQ significantly impact customer satisfaction, supporting the IS Success model as being a measure of IS success. Baabdullah et al. [49] investigated the factors predicting the use of mobile banking and their impact on customer loyalty and satisfaction in the context of Saudi Arabia. Using an iterated model that includes the D&M ISS model, they found that SQ and SRQ (from the IS Success model) have a significant association with the actual utilization of mobile banking among Saudi consumers. Within the context of fintech apps’ success, Riantama et al. [51] employed the D&M ISS model to evaluate the moderating role of trust in the relationship between SQ, IQ, SRQ, and consumer SAT with fintech apps in Indonesia. Their findings revealed that IQ had the most significant influence on consumer satisfaction. They also found that SRQ, when combined with trust, had a stronger impact on determining consumer satisfaction. A combination of the D&M ISS model with the self-determination theory was utilized by Rahi and Abd. Ghani [52] to investigate the continuance intentions of internet banking consumers. They found that the customers’ intentions were determined by multiple factors such as IQ, SRQ, SAT, and various types of regulations. Similarly, Angelina et al. [53] investigated how consumer satisfaction and behavior were affected by varying levels of SQ, SRQ, and IQ in e-commerce apps. Their results, based on the D&M ISS Model, showed that consumer satisfaction was significantly influenced by both SQ and SRQ, while IQ had little to no impact on satisfaction levels. According to the studies provided, the D&M ISS model has demonstrated its practicality in explaining consumer behavior and satisfaction toward emerging technologies. Thus, it could be effectively applied to investigate the D&M ISS Model in the context of BNPL apps in this particular study. Thus, the following hypotheses were formulated:

H6. 

SQ has a positive impact on Gen Z consumers’ satisfaction with BNPL apps.

H7. 

SRQ has a positive impact on Gen Z consumers’ satisfaction with BNPL apps.

H8. 

IQ has a positive impact on Gen Z consumers’ satisfaction with BNPL apps.

Satisfaction is commonly expressed by consumers after they have made a purchase [53]. Several studies define satisfaction (SAT) as the way in which consumers evaluate their level of happiness or unhappiness according to whether they obtained all of the expected benefits from engagements with an e-commerce platform (e.g., [52,54]). Studies on business-to-consumer (B2C) e-commerce suggest that a person’s continuance intention (CI) is essential for prolonged success (e.g., [55]). Likewise, research on mobile apps has found a positive correlation between consumer satisfaction and CI regarding mobile apps, indicating that consumers are more likely to engage with mobile apps when they are highly satisfied with their experience (e.g., [41,42,43]). Therefore, it is anticipated that the satisfaction of consumers with BNPL apps will play a vital role in influencing their intention to continue using them. This expectation is expressed as the following hypothesis:

H9. 

Gen Z consumers’ satisfaction with BNPL apps has a positive impact on their intention to continue using these apps.

3.3. Integrated Theories

Some scholars have suggested combining theoretical models or frameworks for specific information systems to better analyze the adoption and continuance intention of a technology (e.g., [56]). Previous relevant research on mobile banking adoption in the Saudi Arabian context has integrated the UTAUT and D&M ISS models to better understand the use of m-banking from Saudi consumers’ perspectives [49]. Therefore, integrating the UTAUT and D&M ISS models is believed to be beneficial to better understand the continuance intention of BNPL apps, especially among the youth population in Saudi Arabia, which constitutes a high proportion of the country and is one of the key markets for fintech apps in the Middle East. This multi-theory approach enriches the existing literature on BNPL app usage and is expected to enhance the model’s predictive power in analyzing consumers’ intentions to continue using BNPL apps, and their satisfaction. The conceptual framework of this paper includes inherited independent variables from the UTAUT model, namely PE, EE, SI, and FCs, and independent variables generated from the D&M ISS model, namely SQ, SRQ, IQ, and SAT, as well as CI as a dependent variable. The conceptual framework is depicted in Figure 1.

4. Methodology

A survey questionnaire was employed in this study. The survey’s questions were adapted from earlier research to better serve the goal of this investigation, which was to determine the variables influencing the continuing intention to use BNPL apps among Gen Z consumers in Saudi Arabia. All of the questions in the survey were close-ended and respondents were asked to choose from multiple-choice answers. The participants were asked to rate each question using a 7-point Likert scale, where 1 indicated a strong disagreement, 2 represented disagreement, 3 indicated a slight disagreement, 4 meant they neither agreed nor disagreed, 5 represented a slight agreement, 6 indicated agreement, and 7 represented a strong agreement. The scale items were translated into Arabic applying Brislin’s back-translation method to ensure accuracy [57]. A team of experts reviewed and confirmed the appropriateness of the translated questionnaire to measure the variables. There was pilot research study carried out prior to the main survey with 35 Gen Z individuals from Saudi Arabia who had experience using BNPL apps. Most respondents found the questionnaire easy to understand and complete in a short time.

Table 1. Scale items of the selected constructs.

Construct	Items	Source
Performance Expectancy	PE1 I find BNPL platforms to be useful in my daily life. PE2 Using BNPL platforms increases my chances of achieving tasks that are important to me. PE3 BNPL platforms help me accomplish tasks more quickly. PE4 Using BNPL platforms increases my productivity.	[30]
Effort Expectancy	EE1 Learning how to use BNPL platforms is easy for me. EE2 My interaction with BNPL platforms is clear and understandable. EE3 I find BNPL platforms easy to use. EE4 It is easy for me to become skilled at using BNPL platforms.	[30]
Social Influence	SI1 People who are important to me think that I should use BNPL platforms. SI2 People who influence my behavior think that I should use BNPL platforms. SI3 People whose opinions that I value prefer that I use BNPL platforms.	[30]
Facilitating Conditions	FC1 I have the resources necessary to use BNPL platforms. FC2 I have the knowledge necessary to use BNPL platforms. FC3 BNPL platforms are compatible with other technologies I use. FC4 I can get help from others when I have difficulties using BNPL platforms.	[30]
System Quality	SQ1 BNPL platforms quickly load all of their text and graphics. SQ2 BNPL platforms are user-friendly. SQ3 BNPL platforms are easy to navigate. SQ4 BNPL platforms are visually attractive. SQ5 I would find BNPL platforms secure enough to conduct my payments.	[46,58,59]
Service Quality	SRQ1 The level of service quality I receive from BNPL platforms is high. SRQ2 The quality of the service I receive from BNPL platforms is excellent. SRQ3 BNPL platforms provide a high level of service quality.	[46,58,59]
Information Quality	IQ1 BNPL platforms provide me with information relevant to my needs. IQ2 BNPL platforms provide me with sufficient information. IQ3 BNPL platforms provide me with accurate information. IQ4 BNPL platforms provide me with up-to-date information.	[46,58,59]
Satisfaction	SAT1 I am generally pleased with BNPL platform services. SAT2 I am very satisfied with BNPL platform services. SAT3 I am happy with BNPL platforms. SAT4 I am satisfied with the way that BNPL platforms carry out transactions. SAT5 Overall, I am satisfied with BNPL platforms.	[46,58,59]
Continuance Intention	CI1 I will continue to use BNPL platforms on a regular basis in the future. CI2 I expect that my use of BNPL platforms for handling my financial transactions will continue in the future. CI3 I intend to continue to perform small payments on BNPL platforms.	[46,59]

summarizes the scale items of the selected constructs.

An online questionnaire was utilized to gather primary data for the empirical investigation, which took place in Saudi Arabia. We used Google Forms to survey Gen Z representatives in June and July 2023 to learn more about their preferences for BNPL apps. The web links were sent to high-school and college students’ online groups on social media to reach the target population, which was between the ages of 14 and 27. The present study utilized convenience sampling, which is considered to be cost-effective and allows for the inclusion of a diverse range of technology users in the sample, facilitating generalization of the results [60]. Prior studies have verified the use of convenience sampling in quantitative survey approaches (e.g., [39,61]). To enhance sample representativeness and address any issues of sampling bias, a larger sample size was necessary. This method was used to create the sample without putting any numerical restrictions on the degree of representation of particular demographic factors such as gender, income, or place of residence. However, the distributed questionnaire was only intended for respondents who were members of Gen Z, which prompted us to point out the targeted nature of the sampling. A total of 722 surveys were collected, and 380 of them were completed, receiving a response percentage of 52.6%. Kline [62] asserts that a research sample size of 200 to 400 is enough for carrying out statistical tests in a complicated study model with numerous variables, which applies to the current study.

One of the most preferred techniques for confirmatory factor analysis (CFA) in social science research is Structural Equation Modeling (SEM), which enables researchers to assess the validity of connections between components and relationships among different hypotheses within the same model [60]. Hence, SEM was utilized in the present research, and statistical software (i.e., SPSS version 26 and SmartPLS 4) was used to analyze the results. During the data analysis via Smart PLS, the two-stage approach was employed as several studies have confirmed the appropriateness of this approach for evaluating model goodness-of-fit and for evaluating and confirming a presented hypothesis [60]. In the first stage, the measurement model was assessed by examining several key indicators such as Cronbach’s alpha, composite reliability, average variance extracted, factor loadings, construct reliability, and discriminant validity. Once the evaluation of the measurement model yielded satisfactory results, we moved to the second stage, which focused on conducting the structural model and hypothesis testing.

5. Results

5.1. A General Overview

Demographic data from the respondents, including age, gender, and education, were gathered for the current study. Among the participants, the females are 59.8% and the males are 40.2%. In terms of age distribution, the largest group of the sample are aged between 21 and 27 years old (70.6%). A high percentage of this age group held a Bachelor’s degree, with 45.1% of the total sample being educated to that level, followed by high-school graduates at 24.5%. This is logical, given that Gen Z is the focus of this investigation. The demographic details are presented in

Table 2. General information.

Category	Details	Percentage
Gender	Male	40.2%
	Female	59.8%
Age	14 to 20 years old	29.4%
	21 to 27 years old	70.6%
Educational level	Lower than high school	4.9%
	High school	24.5%
	Diploma	10.2%
	Bachelor’s degree	45.1%
	Master’s degree	15.3%
I use fintech payment apps because it is:	Simple to make payments	54.6%
	More flexible	40.6%
	Free of interest	39.7%
	Simple to receive approval	31.2%
Products or services I have purchased with BNPL are:	Clothing	77.3%
	Entertainment	23.7%
	Books	14.5%
	Furnishing	28.2%
	Groceries	5%
	Food delivery services	5%
	Electronics	41.4%
	Travel tickets	6.5%

.

When asked why they use BNPL apps, research participants had a choice of many responses. Most responders claimed that the reasons they picked BNPL apps were that they were simple to use for making payments (54.6%), more flexible (40.6%), interest-free (39.7%), and had an easy approval procedure (31.2%). Furthermore, respondents were asked about the type of products or services they purchased with BNPL apps, with the option to choose multiple answers. The most commonly purchased products were clothing (77.3%), followed by electronics (41.4%), household furnishings (28.2%), entertainment (23.7%), books (14.5%), and travel tickets (6.5%), followed by groceries and food delivery services, with (5%) for each of these categories.

5.2. Descriptive Statistics

To evaluate the outcomes of the variables, descriptive statistics were employed to compute the means and standard deviations of the researched constructs. Determining the distribution shape of the examined constructs by conducting a normality check as the first stage in the investigation is vital [60]. The findings of a statistical test may be invalidated if a normal distribution is not produced. Kurtosis and skewness, which evaluate the distribution’s peak level and balance, respectively, were studied [60]. Skewness and kurtosis values for a normal distribution are both 0. Hair et al. [60] determined that the permissible limit for kurtosis and skewness is 2.58. In this study, all of the tested constructs had acceptable ranges.

5.3. Common Method Bias

By utilizing a self-report method, the respondents were asked to submit answers to items for the independent and dependent factors [63]. This raises concerns about common method bias. The study questions for each of the nine examined constructs (PE, EE, SI, FCs, SQ, SRQ, IQ, SAT, and CI) were subjected to analysis using Harman’s single-factor analysis in order to overcome this problem [63]. Only 38.25% of the variation falls under the 50% cutoff point suggested by [63]. Therefore, the data from this investigation do not show any substantial indicators of common method bias, according to the findings of Harman’s single-factor analysis.

5.4. Measurement Model

A literature review was used to create the model, and adoption metrics for fintech payment applications were selected. The reliability and validity of the examined variables were first evaluated. Cronbach’s alpha, Omega reliability, and composite reliability (CR) were used to evaluate reliability. The standards for construct reliability and validity all met the fundamental criteria, as shown in

Table 3. Cronbach’s alpha, Omega reliability ω, composite reliability (CR), and average variance extracted (AVE).

	Cronbach’s Alpha	Omega Reliability ω	Composite Reliability (CR)	Average Variance Extracted (AVE)
PE	0.879	0.881	0.917	0.734
EE	0.916	0.920	0.924	0.803
SI	0.893	0.895	0.942	0.845
FCs	0.844	0.847	0.933	0.824
SQ	0.906	0.910	0.930	0.726
SRQ	0.935	0.938	0.959	0.885
IQ	0.895	0.897	0.934	0.921
SAT	0.894	0.896	0.957	0.817
CI	0.895	0.898	0.935	0.828

[60,62]. It was found that the Cronbach’s alpha values were higher than 0.70, the threshold advised by [60]. In this regard, FCs had the lowest value (0.844), and SRQ had the greatest value (0.935) for Cronbach’s alpha. Furthermore, McDonald’s Omega was developed to assess reliability. The results show that all scale items displayed significant internal consistency, as evidenced by the McDonald’s Omega values being more than 0.70 [64]. Additionally, the CR values of the nine examined variables were over 0.70, the threshold suggested in [60]. The PE sample had the lowest CR value of 0.917, whereas the SRQ sample had the highest CR value of 0.959. Additionally, [60] recommended that the average variance extracted (AVE) values for all nine latent components should be above 0.50. SQ had the lowest AVE (0.726), while IQ had the highest AVE (0.921). Table 3 present the tests results of the Cronbach’s alpha, Omega reliability, CR, and AVE.

All factor loadings of the scale items were above 0.50, according to the analysis’s findings, with the lowest value coming in at 0.750. Furthermore, the square root of the AVE, which should be above 0.70 as suggested in [60], ranged from 0.852 for SQ to 0.960 for IQ and fell within the acceptable range. The factor loadings, the CR, the AVE, and the square root of AVE are shown in

Table 4. Factor loadings, construct reliability, and average variance extracted.

Items	FL	CR	AVE	Sqrt AVE
Performance Expectancy		0.917	0.734	0.857
PE1	0.861
PE2	0.847
PE3	0.889
PE4	0.829
Effort Expectancy		0.924	0.803	0.896
EE1	0.907
EE2	0.894
EE3	0.914
EE4	0.859
Social Influence		0.942	0.845	0.919
SI1	0.913
SI2	0.906
SI3	0.903
Facilitating Conditions		0.933	0.824	0.908
FC1	0.815
FC2	0.839
FC3	0.896
FC4	0.750
System Quality		0.930	0.726	0.852
SQ1	0.821
SQ2	0.861
SQ3	0.861
SQ4	0.858
SQ5	0.859
Service Quality		0.959	0.885	0.941
SRQ1	0.939
SRQ2	0.947
SRQ3	0.937
Information Quality		0.934	0.921	0.960
IQ1	0.877
IQ3	0.923
IQ4	0.925
Satisfaction		0.957	0.817	0.904
SAT1	0.894
SAT3	0.919
SAT5	0.912
Continuance intention		0.935	0.828	0.910
CI1	0.938
CI2	0.928
CI3	0.862

Note(s): factor loading (FL), construct reliability (CR), average variance extracted (AVE), square root of average variance extracted (Sqrt AVE).

.

By examining the square root of the AVE for each variable with the latent variables’ inter-correlation values, discriminant validity was evaluated. The square root of the AVE of all examined constructs should be above the inter-correlation values of each construct. The results suggest that the scale and components employed in the present investigation satisfy the requirements for discriminant validity [65]. The measuring model demonstrates the suitability of the selected constructs for the model (

Table 5. Discriminant validity assessment and heterotrait–monotrait ratio of correlations (HTMT).

	BI	EE	FCs	IQ	PE	SAT	SI	SQ	SRQ
CI	0.910
EE	0.456	0.896
FCs	0.656	0.836	0.908
IQ	0.581	0.582	0.748	0.960
PE	0.65	0.709	0.782	0.586	0.857
SAT	0.899	0.621	0.768	0.703	0.694	0.904
SI	0.622	0.335	0.623	0.564	0.618	0.577	0.919
SQ	0.674	0.712	0.808	0.894	0.63	0.845	0.527	0.852
SRQ	0.667	0.627	0.733	0.727	0.598	0.812	0.501	0.809	0.941

Note(s): Bold values are the square roots of the AVE.

).

5.5. Structural Model

The model fit statistics were examined using the basic CB-SEM algorithm in SmartPLS 4. The findings presented a suitable level of fit. The chi-square was 194.369, GFI = 0.958, AGFI = 0.923, NFI = 0.954, TLI = 0. 951, CFI = 0.957, and RMSEA = 0.062. Therefore, the findings of all of the goodness-of-fit indices were within an acceptable range (

Table 6. Model fit indices.

Fit Indices	Threshold Value	Fit Index Value
Chi-square		194.369
GFI	≥0.95	0.958
AGFI	≥0.90	0.923
NFI	≥0.95	0.954
TLI	≥0.95	0. 951
CFI	≥0.95	0.957
RMSEA	<0.08	0.062

).

The majority of the research hypotheses were supported by the route coefficient analysis because they were determined to be significant. The hypotheses were assessed using the critical t-value analysis of the path estimations.

Table 7. Hypothesis testing.

Hypothesis	Path	Path Coefficient	β	t-Statistic	p-Value	Empirical Evidence
H1	PE→CI	0.239	0.279	3.030 **	0.002	Supported
H2	EE→CI	0.127	0.241	2.312 **	0.021	Supported
H3	SI→CI	0.221	0.273	1.999 **	0.046	Supported
H4	FCs→CI	0.160	0.015	1.543 n.s.	0.123	Not supported
H5	EE→PE	0.640	0.735	19.682 ***	0.000	Supported
H6	SQ→ SAT	0.430	0.474	4.709 ***	0.000	Supported
H7	SRQ→SAT	0.347	0.351	4.067 ***	0.000	Supported
H8	IQ→SAT	0.004	0.082	1.556 n.s.	0.120	Not supported
H9	SAT→CI	0.540	0.547	10.314 ***	0.000	Supported

Note(s): ** p < 0.01, *** p < 0.001, n.s. not significant.

displays the results of the hypothesis testing. The findings supported seven of the nine investigated hypotheses. H4 and H8 were found to be not significant.

Hair et al. [60] suggested that R² values were used to assess the extent to which the model explained the variance in the dependent variable. Figure 2 displays the correlation R² values for each construct in the model, which shows that 60.3% of the variance in fintech payment application adoption was explained by the variable CI (R² = 0.603). Additionally, the results show that the model can provide explanations for 60.3% of the variance in CI, and 81.7% of the variance in satisfaction (R² = 0.603 and 0.817, respectively).

6. Discussion and Contributions

The Saudi government is increasingly concerned about the growth of BNPL apps, given their contribution to achieving its 2023 Vision. This growth is fueled by the satisfaction provided by these apps and consumers’ continued usage of them, particularly Gen Z consumers who are native to digital platforms. This, in turn, motivated the current study to test aspects that determine the adoption and satisfaction of BNPL apps among Gen Z.

In order to empirically examine the factors impacting Gen Z consumers’ intentions to continue using BNPL apps, this study used an integrated approach. The recently created model incorporates elements from the UTAUT and D&M ISS models. A two-stage SEM process was utilized to examine this model. According to the findings, this model is capable of accurately predicting the continuance intentions of Gen Z consumers to employ BNPL apps and recording their satisfaction levels with such apps.

It was found that PE and EE had a significant influence on Gen Z continuance intention toward BNPL app adoption; therefore, H1 and H2 were supported. Such results are similar to the findings of Jagadhita et al. [13], who found a significant influence of perceived usefulness and ease of use on usage intention of BNPL in Indonesia, but contradict the findings of Hidayat et al. [14].

In addition, SI showed a significant positive affect on Gen Z continuance intention toward BNPL apps and H3 was also supported. These outcomes are consistent with earlier research such as that of Hidayat et al. [14], Chan et al. [35], and Xie et al. [66]. However, H4 was not supported, as the influence of facilitating conditions on Gen Z continuance intention toward BNPL apps was found to be insignificant, consistent with earlier research by Aydin and Kumru [67]. A possible explanation for this is that Gen Z are digital natives and can use BNPL apps without significant issues or needing support. Furthermore, it was discovered that the EE of BNPL apps positively influences the PE; this supports H5. These outcomes are in line with those of earlier studies [45,68]. Overall, it can be concluded that based on UTAUT, the intention to continue using BNPL apps among Gen Z consumers in Saudi Arabia is significantly influenced by the PE, EE, and SI.

The D&M ISS model was added in order to expand the UTAUT. The results of the SEM analysis show that SQ and SRQ have a big impact on Gen Z’s satisfaction. This supports H6 and H7 and is in alignment with other studies by Yoon and Kim [54] and Lai [55]. However, the effect of IQ on satisfaction was found to be not significant and H8 was not supported. Similar results were found by Baabdullah et al. [49] in their study of mobile banking adoption among Saudis.

This study also examined the effect of Gen Z’s satisfaction on continuance intention to adopt BNPL apps, and the findings show a strong correlation. These results support H9 and are in line with past research by Gu et al. [41], Marinković et al. [42], and Puriwat and Tripopsakul [43]. Overall, seven out of the nine hypotheses were supported, indicating that this study’s model is effective in accurately predicting the continuance intention of Gen Z consumers to adopt BNPL apps, and their satisfaction levels, in Saudi Arabia.

6.1. Theoretical Contributions

This study provides significant theoretical contributions to the existing literature through the integration of two prominent models—the D&M ISS model by DeLone and McLean [46] and the UTAUT model by Venkatesh et al. [30]. As suggested in [30], we explored the application of UTAUT in a novel technological context and further combined it with the D&M ISS model. Integrating these two models in the context of BNPL apps has been uncommon in previous research. Furthermore, by offering a conceptual model that simplifies and explains the use of BNPL from the consumer’s perspective, this study helps to bridge this gap in the existing literature. Although similar models have been used in a few studies—such as in examining the acceptance of mobile banking in Islamic banks in Palestine [69]—our approach represents a novel application of the integrated model in the BNPL context of Saudi Arabia and Gen Z consumers. Furthermore, while previous studies have explored this generation’s attitudes toward fintech apps in various contexts, none of these studies have employed an integrated model such as the UTAUT–D&M ISS model we have used in this research, to the best of our knowledge. Studies have explored Gen Z’s intentions to use fintech payment services and BNPL in Vietnam (e.g., [70]) and Indonesia [13,14], albeit with different models and factors considered. Additionally, our research considers several variables that have not previously been explored in the BNPL literature but may have an impact on consumers’ usage of BNPL. Moreover, this study bridges a gap in identifying the factors that contribute to the satisfaction and continuance intention of BNPL consumers, particularly in the context of Saudi Arabia and among Gen Z. This is significant because Saudi Arabia is considered a youth country and one of the fastest-growing app markets in the Middle East. Despite the country’s investment in developing BNPL and other fintech payment services, not a single study has focused on this aspect to date. In particular, this paper looks at how BNPL apps are being used in Saudi Arabia, with an ultimate goal of supporting the country’s progress toward achieving its SDGs by 2023. By empirically evaluating and identifying the crucial factors that motivate Gen Z to keep using BNPL apps, this study lays a solid platform for future research and provides evidence to support the country in achieving its 2023 SDGs by 2030. By empirically evaluating and identifying the crucial factors that motivate Gen Z to keep using BNPL apps, this study lays a solid platform for future research and provides evidence to support the country in achieving its 2030 SDGs with regard to financial inclusion and fintech development.

6.2. Practical Contributions

From theory to practice, this study underscores the critical need for designers and developers to prioritize enhancing the usability and appeal of BNPL apps in order to effectively engage Gen Z consumers who use them. First and foremost, apps need to be well structured so that consumers can easily explore them and realize how useful they are. They should also include information on each consumer, such as their names and transaction history. Furthermore, marketers should use public figures and influencers to promote different BNPL apps, as the results indicate that consumers’ surroundings and social groups would affect their continuance intention to use BNPL apps. Furthermore, Gen Z consumers emphasize the SQ and SRQ components of the D&M ISS model when utilizing BNPL apps in the current digital environment where security is a priority. To reduce any usage uncertainty, BNPL app developers should concentrate on maintaining a stable system performance. Gen Z consumers are looking for BNPL apps that are secure, simple to use and make payments on, more flexible, and interest-free; that have an easy approval procedure; and that open quickly on mobile devices. To match consumer expectations, developers must offer and frequently update app versions. Furthermore, application developers should take in to consideration the ethical implications of promoting BNPL usage among Gen Z consumers as it could lead to overconsumption or debt risks. This includes implementing responsible lending practices to prevent over-indebtedness, giving consumers control over their accounts and payments by allowing them to track their expenses and debt in real time, and educating Gen Z consumers about BNPL services and the potential risks involved. By incorporating these considerations into BNPL services, application developers can create a user-friendly experience for Gen Z consumers that prioritizes responsibility, control, and education.

Developers and marketers should pay attention to these vital components as consumers’ satisfaction will affect consumers’ intentions to continue to use BNPL apps. Implementing the recommended practical implications can enhance the financial inclusion of Gen Z consumers, facilitating their access to alternative financial services.

This study’s results have crucial implications for policymakers. First, the research findings demonstrate that the UTAUT constructs and D&M factors impact consumers’ decisions to adopt BNPL apps, emphasizing the need to consider these influences when developing policies and strategies to encourage app usage among consumers, especially among Gen Z. Second, policymakers can consider implementing regulations to ensure that BNPL apps focus on enhancing performance expectancy, effort expectancy, and social influence to drive consumer intent for continuous usage. Regulations could also mandate improvements in system, service, and information quality within BNPL apps to enhance consumer satisfaction and encourage ongoing use. Furthermore, policymakers can enforce measures to promote and monitor consumer satisfaction with BNPL apps to support sustainable usage and positive outcomes for consumers. This balanced approach may help mitigate the financial risks associated with the overutilization of BNPL for non-essential products, ensuring financial inclusion and working towards achieving the Sustainable Development Goals for Gen Z. By implementing these strategies, the growth of the fintech industry can be facilitated, helping Saudi Arabia achieve its target of having 525 fintech companies by 2030.

7. Limitations and Future Research Directions

It is crucial to recognize the limits of this research. First, the goal of this study is to advance knowledge of the continuance intention to use BNPL apps in Saudi Arabia. The results may not be applicable to other nations because they are specific to the Saudi Arabian environment. Future studies should examine this subject in various geographic and cultural settings. Second, this study conducted a survey-based quantitative approach by relying on self-reported data without corroborating qualitative insights. Future research could be conducted using a mixed-methods approach by incorporating qualitative methods to gain better insights and in-depth views of the findings along with the quantitative surveys. Third, the results’ generalizability might be constrained by the convenience sampling technique employed in the current research. Future research should look at consumer histories of using BNPL apps to address this issue and make use of different sampling strategies in order to ensure a more representative sample. Fourth, this study focuses on examining the usage behavior of prominent technology consumers, specifically Gen Z, the reason for this being that individuals of this generation are known for having a high-consumption, instant-gratification lifestyle, but limited income, making BNPL an appealing option for their needs. In the future, similar studies can focus on other demographic groups such as Millennials, who are also known to adopt technologies, or conduct a comparison between different demographic groups. Fifth, the cross-sectional research design utilized in the present research prevents the assessment of attitudes that are gradually shifting. To better understand how views and behaviors surrounding BNPL app adoption change over time, a longitudinal study is required. Finally, this study has explored the consumer perception of BNPL and its potential connection to Sustainable Development Goals (SDGs) through increased financial inclusion. While this research has not provided empirical evidence of the influence of BNPL on financial inclusion, it serves as a foundation for future studies to investigate this relationship further. Finally, this study explored the consumer perspective on BNPL adoption from a technological standpoint. Future research can delve deeper into other aspects, such as overconsumption or debt risks.

8. Conclusions

In order to evaluate the important drivers of continuing intentions for BNPL apps, this study focuses on Saudi Arabia, one of the main markets for mobile applications (apps) in the Middle East. It also focuses on Gen Z, given their important contribution to this specific fintech sector. To examine these drivers, we integrated the UTAUT and D&M ISS models, two popular information systems theories, to comprehend the specific effects of BNPL apps on consumer satisfaction and continuance intention. A sample poll was conducted, and 380 Generation Z consumers participated. The findings suggest that performance expectancy, effort expectancy, and social influence are key drivers of continuance intention, while system quality, service quality, and information quality impact consumer satisfaction. Additionally, effort expectancy has a favorable effect on the performance expectancy of BNPL apps. Satisfaction has a direct impact on whether Gen Z consumers decide to keep using BNPL apps. This study’s findings suggest that app developers should prioritize usability, security, and simplicity for Gen Z, while policymakers should consider regulations promoting sustainable app usage. This study aims to inform regulators and fintech providers about Gen Z’s perspectives on BNPL apps, supporting their efforts to foster financial inclusion, a key component of the Sustainable Development Goals (SDGs). Future research should address limitations, including financial risk perceptions, to ensure sustainable usability among Gen Z consumers.