The Role of ESG Awareness in Green FinTech Adoption Among Generation Z: Evidence from Saudi Arabia

Abstract

In this study, we examine how Environmental, Social, and Governance (ESG) awareness influences Generation Z’s adoption of Green FinTech services, especially in digitally emerging economies such as Saudi Arabia. Drawing on an extended model of the Unified Theory of Acceptance and Use of Technology (UTAUT), ESG awareness is conceptualized as both a direct predictor and a contextual moderator by surveying 347 users and analyzing the data using PLS-SEM. The results show that ESG awareness significantly enhances behavioral intention to adopt Green FinTech among Gen Z users. In addition, ESG awareness strengthens the effects of the UTAUT adoption predictors. The study contributes to the sustainability field by integrating ESG cognition into technology adoption theory. In addition, the study provides insights for decision makers and regulators who aim to increase and promote the adoption of sustainable FinTech and digital services.

1. Introduction

In alignment with global initiatives such as the United Nations Sustainable Development Goals (SDGs), emerging financial technologies have been increasingly recognized as key enablers of environmental sustainability. To that extent, financial institutions across the glove need to integrate the ESG framework and practices into both their operational and strategic plans. These technologies facilitate the adoption of eco-friendly financial infrastructures while delivering environmental, social, and economic benefits [1,2]. This has given rise to the Green FinTech movement in different organizations, particularly in the FinTech sector, where digital technologies are increasingly tailored to environmentally conscious users [3].

Technological innovation in the financial industry has been a driver of technology transformation that started with online banking and advanced to Green FinTech systems [4]. Today, aiming beyond just profits, many financial institutions use the ESG framework as a strategic goal. Adopting eco-friendly digital services allows banks to address wider social and environmental ESG objectives [5]. Green FinTech encompasses not only energy-saving activities but also extends to include the digitization of business processes and activities, financing of sustainable projects, and many other green initiatives [6,7]. ESG awareness is a multidimensional construct reflecting individuals’ understanding of the environmental, social, and governance implications of financial behavior. This distinction separates ESG Awareness [8,9,10,11] from other constructs in the sustainability literature, such as environmental consciousness (an affective concern) [12,13] and sustainability knowledge (a cognitive familiarity with sustainability facts) [14,15,16,17].

With ambitious sustainable goals, Saudi Arabia has been endeavoring to be an active participant in the international community, adopting and promoting ESG efforts by laying down a strategic framework in its new 2030 countrywide vision [18]. This framework encourages eco-friendly digital transformation and promotes sustainability in business practices. In addition, the Kingdom launched the Sustainable Finance Framework to support institutions in adopting the principles of green banking practices [19]. Many financial institutions in Saudi Arabia are already embracing Green FinTech to support ESG goals [4]. Although there are governmental and organizational efforts, a recent paper by Piotrowska and Piotrowski [20] raised concerns about limited consumer awareness of the environmental benefits of Green FinTech. Even so, how ESG awareness actually shapes adoption behavior remains a critical question for both theory and practice.

To address this gap, our study explores how ESG awareness shapes Green FinTech adoption among Gen Z users, particularly in traditionally oil-dependent economies. The paper is organized as follows: Section 2 summarizes and synthesizes the relevant literature and related studies. Section 3 outlines the theoretical foundation and the development of the conceptual model. Section 4 describes the methodology used in the underlying research. Section 5 presents the findings. Section 6 discusses the results and their implications. Section 7 discusses limitations and highlights potential future avenues for research. In the last section, the conclusions are presented.

2. Literature Review and Underpinning Theory

Although Green FinTech is a growing field, most of the studies in the existing body of literature focus on the general population. Gen Z remains a significantly understudied user group, which limits our understanding of what shape their behavior takes, especially in countries with a high percentage of young consumers [21,22]. In addition, there is a dearth of research regarding the role of ESG awareness in oil- and gas-dependent economies. Our research aims to bridge the existing gap by putting the focus on young consumers aged 18–30 years old and investigating how ESG awareness impacts their behavioral intentions. These individuals have unique characteristics as technology natives; their behavioral choices carry significant implications for the long-term trajectory of sustainability efforts [23]. They are also known as technology migrants and have high levels of environmental sensitivity compared to other groups [24]. Gen Z’s opinions and practices influence the overall long-term effect of positive environmental practices in different areas, including banking. Saudi Arabia’s rapid transition into its digital financial era has highly promoted the adoption of Green FinTech, such as digital banking services. These services have encouraged the country to get closer to achieving its green vision [25,26]. At the same time, the environmental motivations of young Saudi clients adopting mobile banking have received little scholarly attention.

Rooted in the Information Systems literature, the Technology Acceptance Model by Davis [27] and the Theory of Reasoned Action by Ajzen & Fishbein [28] both indicate that attitude is a crucial predictor of a user’s behavioral intention. In addition, more recent studies confirm this relationship using empirical methods that investigate the relationship in the context of digital and mobile financial services. For instance, Baabdullah et al. [29] found that favorable attitudes toward mobile banking in Saudi Arabia come from consumers’ perceptions of usefulness and ease of use, which together shape customers’ intentions to adopt digital services. Similarly, in a recent study by Alnemer et al. [30] in Saudi Arabia, the authors established that positive attitudes impact users’ willingness to adopt digital banking. A recent paper by Hilal and Varela-Neira [31] also supported the stability of such a relationship. The scholars established that attitudes influenced by ease of use, usefulness, and personal character can strongly predict if a user would adopt mobile banking for their daily usage. A study by Zhang et al. [32] in the area of FinTech further highlights that user attitudes are shaped not only by functional benefits but also by trust and perceived ethical alignment. The recent literature has also emphasized the broader role of sustainability disclosure and environmental responsibility in shaping financial behaviors [33,34], reinforcing the importance of integrating ESG-related constructs into technology adoption frameworks. While prior studies have examined FinTech adoption and sustainability independently, there remains a notable gap in understanding how ESG awareness interacts with established technology adoption models to shape behavioral intentions, especially among Generation Z, in the context of Green FinTech. Furthermore, the empirical evidence on Green FinTech adoption within oil-dependent and rapidly digitizing economies such as Saudi Arabia remains scarce, particularly regarding the role of ESG awareness among Generation Z consumers.

3. Hypothesis Development

In this study, we drew upon the Unified Theory of Acceptance and Use of Technology (UTAUT), which has been widely used to investigate customer acceptance and the use of emerging technologies [32,35,36]. To align with these objectives, Saudi sustainability efforts emphasize digital transformation and environmental sustainability as goals for the nation’s commercial and governmental organizations. A conceptual model has been developed that incorporates key UTAUT constructs and extends them with ESG awareness as a direct enabler for behavioral intention and a moderator for the UTAUT constructs. The extended research model in Figure 1 is discussed in the following sections.

3.1. ESG Awareness and Behavioral Intentions

The UN SDGs were the global response to preserve the environment and enhance the quality of life for individuals and societies. Financial institutions are central to most economies and are well-positioned to champion eco-friendly practices [20,37]. FinTech services have been available for several years now, but in Saudi Arabia and the Arabian Gulf region, they have grown recently due to an increase in smartphone use, improved internet infrastructure, and strong governmental support for digital transformation, especially in green technologies [29,38]. Although many customers engage with FinTech and digital services due to their utilitarian and functional benefits [38,39], it has been argued in recent studies, such as those of Alkhowaiter [40] and Dutta and Shome [10], that environmental motivations due to ESG awareness are emerging as influential factors in determining consumers’ behavior. In addition, recent studies have found that ESG awareness can significantly shape users’ motivation towards consumerism and adoption of eco-friendly digital tools [10,41,42]. These considerations lead to the following hypothesis:

H1. 

ESG awareness positively affects customers’ behavioral intention to adopt Green FinTech.

3.2. UTAUT and Green FinTech

Customers may view these innovations as complicated: as banks shift traditional services to digital platforms, particularly green mobile banking apps, the increased level of automation necessitates higher levels of self-efficacy. PE, or the perceived utility of Green FinTech and effort expectancy, are two crucial UTAUT constructs that are directly impacted by this change. While the influence of effort expectancy is still context-dependent [43], previous research has consistently found performance expectancy to be a dominant predictor of the behavioral intention to use technology [35]. In addition, effort expectancy can significantly influence customer intentions to adopt Green FinTech, where convenience is highly valued. Consumers’ acceptance and adoption behaviors depend largely on the perceived advantages and simplicity of using Green FinTech [37,44]. In a rapidly changing digital landscape, where leadership has been actively fostering a sustainable and tech-driven economy, users are more likely to adopt financial technologies that not only satisfy their functional needs but are also consistent with the larger societal values of digital innovation and environmental responsibility [29,38,45]. Drawing on this reasoning, we propose the following hypothesis:

H2. 

Performance expectancy positively affects customers’ behavioral intention to adopt Green FinTech.

The perceived ease of use of a specific system is known as effort expectancy [46]. Customers’ perceptions of how simple it is to use and complete transactions through environmentally friendly banking apps are reflected in the area of Green FinTech. Previous research has consistently demonstrated that users are more likely to adopt and continue using digital banking platforms when they believe they are easy to use and require little effort [39,47]. the perceived complexity may increase for fewer users with less tech experience, for more sustainability-focused features, like carbon-footprint tracking, digitized services, or paperless statements. However, a well-designed and intuitive interface lowers cognitive barriers, increasing adoption intent. Customers’ behavioral intention to use green banking apps is therefore likely to increase when they believe that these apps are easy to use, accessible, and require little learning effort. Thus, this leads us to propose:

H3. 

Effort expectancy positively affects customers’ behavioral intention to adopt Green FinTech.

According to Venkatesh et al. [46], social influence is the extent to which people believe that significant others—like friends, family, coworkers, or society at large—think they ought to use a specific technology. The social influences from peers and what is known as a normal behavior in a group or a society have a significant influence on how conscious decisions are made regarding sustainable behavior. Customers frequently turn to their social networks or online communities for support and encouragement because green banking is strongly associated with moral and ecological principles. People are more inclined to imitate green financial practices when they are supported or practiced by peers to conform to shared values or gain social acceptance [13,48,49]. This effect is amplified by the increasing pressure to adopt sustainable practices in the new vision of Saudi Arabia, where sustainability has become a social and institutional priority. Consequently, users’ intention to use green FinTech will be significantly influenced by social influence. Based on the arguments and discussions above, the hypotheses below are posited:

H4. 

Social influence positively affects customers’ behavioral intention to adopt Green FinTech.

H5. 

Facilitating conditions positively affect customers’ behavioral intention to adopt Green FinTech.

3.3. ESG Awareness as a Moderator

Despite the increase in the adoption of FinTech, users differ considerably in their level of sustainability awareness, which refers to how individuals understand and value the environmental, social, and long-term economic implications of their financial behaviors. Sustainability-aware consumers tend to evaluate financial technologies not only based on functional efficiency, but also on their contribution to responsible consumption, environmental protection, and sustainable development [46,47]. As such, ESG awareness is expected to shape how traditional technology adoption factors influence behavioral intention in the Green FinTech context. Performance expectancy reflects the extent to which users believe that Green FinTech enhances efficiency, convenience, or financial outcomes [50,51]. While performance benefits are central to technology adoption, users with higher ESG awareness are more capable of recognizing non-financial and long-term value, such as reduced environmental impact, paperless transactions, and support for responsible finance. For these users, performance is not evaluated solely in terms of speed or usability, but also in terms of environmental and social performance. Consequently, ESG-aware and cognizant individuals are more likely to perceive performance gains as aligned with ESG outcomes, thereby strengthening the effect of performance expectancy on behavioral intention. In contrast, users with limited ESG awareness may narrowly focus on short-term functional benefits, resulting in a weaker relationship [52,53]. Based on the arguments and discussion above, the hypothesis below is developed:

H6a. 

ESG awareness positively moderates the impact of performance expectancy on the behavioral intention to adopt Green FinTech.

For ESG-aware individuals, effort expectancy related to ethical consumption, environmental responsibility, and responsible finance carry greater weight. These users are more likely to accept socially driven ESG expectations and translate them into adoption behavior [54,55]. As a result, ESG awareness intensifies the effect of effort expectancy by converting effort pressure into moral and social obligations. In contrast, users with limited ESG awareness may perceive effort expectancy as weak or irrelevant, reducing its impact on behavioral intention [10]. Based on the preceding arguments, we therefore hypothesize:

H6b. 

ESG awareness positively moderates the impact of effort expectancy on the behavioral intention to adopt Green FinTech.

Social influence reflects the perceived pressure or encouragement from peers, family, or social networks [44]. Sustainability-aware individuals are more sensitive to social norms related to environmental responsibility and ethical consumption [55]. Recommendations, endorsements, or expectations from peers and social networks carry greater persuasive power when users already value sustainability principles. In such cases, social influence reinforces moral and social norms associated with sustainable financial behavior, strengthening the link between social influence and behavioral intention. For users with low sustainability awareness, however, social pressure related to green banking may be perceived as weak or irrelevant, reducing its behavioral impact. Based on this reasoning, we therefore hypothesize:

H6c. 

ESG awareness positively moderates the impact of social influence on the behavioral intention to adopt Green FinTech.

Facilitating conditions refer to users’ perceptions of the availability of the technical, organizational, and resource support necessary to use green mobile banking effectively. While facilitating conditions are traditionally viewed as infrastructural enablers, their influence on behavioral intention can vary depending on users’ sustainability awareness. Individuals with high sustainability awareness are more likely to actively recognize and leverage available support mechanisms—such as secure digital infrastructure, customer assistance, and regulatory assurances—when these conditions align with sustainable and responsible banking practices. For these users, the presence of facilitating conditions reduces perceived risk and reinforces confidence in green mobile banking as a viable and trustworthy sustainability-oriented solution. Moreover, sustainability-aware users tend to associate strong facilitating conditions with institutional commitment to ESG principles, interpreting them as signals of long-term reliability and ethical responsibility. This perception strengthens the motivational impact of facilitating conditions on behavioral intention. In contrast, users with lower sustainability awareness may perceive facilitating conditions merely as basic technical requirements, thereby weakening their influence on intention. Based on this reasoning, we expect the following:

H6d. 

ESG awareness positively moderates the impact of facilitating conditions on the behavioral intention to adopt Green FinTech.

3.4. Attitude Towards Green FinTech

ESG awareness reflects individuals’ understanding of environmental, social, and governance matters. Such understanding will influence their values and ethical orientation toward products and services [8,9,10,11]. According to the value-based adoption and attitudinal formation theories, those with ESG awareness may favor Green FinTech and evaluate it positively, since such technologies will better align with their sustainability beliefs and responsibility. In contrast to traditional technology acceptance drivers such as PE, EE, FC, and SI, ESG awareness signifies a normative and value-driven driver that influences ESG-aware individuals [9,55,56]. Accordingly, ESG awareness is expected to positively influence individuals’ attitudes toward Green FinTech. Based on the previous arguments and discussion, this evidence supports the following hypothesis:

H7. 

ESG awareness positively influences attitudes toward Green FinTech.

The TAM [57] and TRA [58] theories both indicate that attitude is a crucial predictor of a user’s behavioral intention. Recent studies confirm this relationship in the context of digital and financial services. For instance, Baabdullah et al. [29] found that favorable attitudes toward mobile banking in Saudi Arabia come from consumers’ perceptions of usefulness, ease of use, and trust, which together shape customers’ intentions to adopt digital services. Similarly, in a more recent study by Alnemer et al. [30], the researchers demonstrated that positive attitudes significantly increase users’ willingness to adopt digital banking platforms within the Saudi context. A paper by Hilal and Varela-Neira [59] also supported the relationship between attitude and behavioral intentions in a similar context. Further, Zhang et al.’s [60] study in the FinTech area further highlights that user attitudes are shaped not only by functional benefits but also by trust and ethical alignment. Based on the previous observations, in the context of Green FinTech, users are more likely to adopt environmentally positive Green FinTech when they perceive it as being aligned with their sustainability values. Based on the previous arguments, accordingly, we hypothesize:

H8. 

Attitude positively affects customers’ behavioral intention to adopt Green FinTech.

4. Methodology

4.1. Sample Collection

This study focuses on young adults in Saudi Arabia who may use digital financial services and technologies. To reach this group, participants were recruited through a convenience sampling approach at a higher educational institution. This approach is justified as university students represent a substantial proportion of Generation Z in Saudi Arabia (approximately 84% [61]) and are key users of FinTech and digital services. Data were collected through surveys, and a total of 364 questionnaires were returned. Informed consent was obtained from all participants before data collection commenced. The responses were examined for missing data, consistency, and completion quality, and 18 responses were removed accordingly. The remaining 347 valid responses, which represent an adequate sample for model estimation, were used as a final data set for the analysis. The survey went through both face and content validity checks to strengthen the instrument. With the help of subject matter experts, the items were reviewed to assess clarity, relevance, and fit. In addition, the questionnaire was piloted with a small group, whose feedback helped refine some wording and improve the flow of the survey. All measurement items were taken from well-established sources. Measurements related to ESG awareness were drawn from earlier studies in green financial services [8,9,10,11]. Behavioral intention items followed commonly used measures in technology adoption research. Well-established scales were used to ensure that the research constructs were grounded in previous empirical research and fit the context of the study.

The questionnaire’s data were imported into Microsoft Excel for data cleansing and then imported into Python 3.11 for PLS-SEM analysis.

Table 1. Respondent profile.

Variable	Response	Frequency	Percent (%)
Sex	Male	211	60.1
	Female	136	39.1
Age	18–21	143	41.2
	22–25	127	36.6
	26–30	80	22.9
Education Level	Undergraduate	289	83.2
	Graduate	58	16.7

summarizes a general demographic overview of the respondents in terms of gender, age, and educational level. As shown in Table 1, males accounted for 61.1% of the participants. The study focuses on Gen Z users, who were born between 1997 and 2012. The majority of the participants belonged to the 18- to 21-year-old age group (40.9%), which is reasonable since the sample consisted mainly of undergraduate students. In addition, 95% of the participants indicated using different types of financial technologies, 64% reported strong familiarity with digital and financial technologies, and 45% of them use these apps on a daily basis.

4.2. Data Analysis

To evaluate the proposed research model, the partial least squares (PLS) technique based on the two-step approach was used. Python statistical packages were used to employ PLS-SEM for the data analysis. PLS-SEM is widely accepted as an appropriate data analysis method for studies with the objective of prediction and theory development. The technique has been widely deployed in the field of sustainability and FinTech [62]. PLS-SEM evaluates the measurement and structural models, and, to ensure methodological robustness, bootstrapping with 5000 resamples was conducted to assess path significance. The structural relationships in the proposed PLS-SEM model are expressed in the equations below:

BItGFA = β₁ ESGA + β₂PE + β₃EE + β₄SI + β₅FC + β_6a(ESGA × PE) + β_6b(ESGA × EE)
+ β_6c(ESGA × SI) + β_6d(ESGA × FC) + β₈AtGF +ε₂

(1)

AtGF = β₇ ESGA + ε₁

(2)

where:

▪

ESGA = ESG awareness;

▪

PE = performance expectancy;

▪

EE = effort expectancy;

▪

SI = social influence;

▪

FC = facilitating conditions;

▪

AtGF = attitude towards Green FinTech;

▪

BItAGF = behavioral intention to adopt Green FinTech;

▪

β = path coefficients;

▪

Ε = error terms.

In Equation (1), β₁ represents the direct effect of ESG awareness on the behavioral intention to adopt FinTech. β₆a–β₆d represent the interaction terms between ESGA and each of the four UTAUT predictors (PE, EE, SI, and FC) in relation to behavioral intention. In Equation (2), β₇ represents the direct effect of ESGA on the attitude toward Green FinTech, independent of the interaction terms specified in Equation (1).

4.3. Common Method Bias

Podsakoff et al. [63] argued that self-reported data may suffer from common method bias. To address this concern, Harman’s test can be performed on all constructs in the conceptual model. Harman’s single-factor test was performed on all constructs, and none of the factors explained more than 50.00%, which is the acceptable limit [63]. This alleviated any CMB concerns. The variance inflation factor was also calculated. None of the research model constructs showed values higher than 5, as indicated by Hair et al. [62].

5. Results

Multicollinearity was evaluated using the variance inflation factor (VIF), with all values below the recommended threshold of 5, indicating no collinearity concerns. Model fit was assessed using the standardized root–mean–square residual (SRMR), which was below the acceptable threshold of 0.08. Convergent and discriminant validity analyses of the constructs were also conducted. The convergent validity was assessed using Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). As illustrated in

Table 2. Validity and reliability.

Construct	Cronbach’s α	CR	AVE
ESG Awareness (ESGA)	0.841	0.858	0.925
Performance Expectancy (PE)	0.863	0.908	0.833
Effort Expectancy (EE)	0.859	0.910	0.852
Social Influence (SI)	0.801	0.857	0.869
Facilitating Conditions (FC)	0.779	0.837	0.863
Behavioral Intention (BI)	0.879	0.916	0.842
Attitude towards Green FinTech (AtGF)	0.911	0.850	0.850

, the measurements were found to exceed the value recommended by Hair et al. [59]. As shown in the table below for the model constructs, rhoA exceeds 0.7 while AVE exceeds 0.5. As demonstrated in the table, a Cronbach’s α greater than 0.70 and CR greater than 0.80 were obtained for all the constructs in the research model, indicating excellent internal consistency. AVE values exceed the 0.50 threshold, confirming convergent validity. Attitude (CR = 0.911) and behavioral intention (CR = 0.879) demonstrate the highest construct reliability, reflecting their central role in explaining users’ adoption behavior. Overall, the measurement model satisfies Hair et al.’s [62,64] criteria for reliability and validity.

After analyzing the data, the research model’s discriminant validity was calculated by comparing the inter-construct correlations to the square root of the average variance extraction (AVE) of corresponding constructs. As presented in

Table 3. Discriminant validity: Fornell–Larcker criterion.

Construct	EA	PE	EE	SI	FC	BI	AtGF
ESGA	0.962
PE	0.348	0.913
EE	0.392	0.638	0.923
SI	0.296	0.694	0.678	0.932
FC	0.342	0.584	0.625	0.512	0.929
BI	0.289	0.872	0.757	0.674	0.563	0.918
AtGF	0.312	0.276	0.198	0.221	0.314	0.281	0.923

Bold: Square root of AVE.

, all the inter-construct correlations values are all lower than the square root of the AVE of the corresponding constructs. This satisfies the criterion for the discriminant validity of the research model according to Fornell & Larcker [65] and further indicates the lack of multicollinearity. All of the HTMT values are below 0.90, as presented in

Table 4. Discriminant validity: Heterotrait–Monotrait (HTMT) scores.

Construct	PE	EE	SI	FC	ESGA	AtGF	BI
PE	—	0.768	0.712	0.735	0.561	0.332	0.498
EE	0.768	—	0.741	0.752	0.583	0.245	0.427
SI	0.712	0.741	—	0.703	0.601	0.271	0.356
FC	0.735	0.752	0.703	—	0.624	0.381	0.318
ESGA	0.561	0.583	0.601	0.624	—	0.372	0.521
AtGF	0.332	0.245	0.271	0.381	0.372	—	0.338
BI	0.498	0.427	0.356	0.318	0.521	0.338	—

. The discriminant validity of the research model are established according to the threshold set by Henseler et al. [66]. Most of the values in Table 4 fall between 0.25 and 0.80. This suggests the empirical differentiation of the constructs despite their conceptual relationships, which corresponds to the theoretical framework of the research.

The study sample size of 347 exceeds the minimum requirements for PLS-SEM, ensuring adequate statistical power for model estimation. The Behavioral Intention R² is 0.651, and the adjusted R² is 0.640, demonstrating the strong explanatory power of the developed conceptual model. Furthermore, the model’s predictive ability, evaluated using the Q2 values, exceeded the recommended threshold of 0.35, which indicates a strong predictive power [62].

Table 5. Hypothesis results.

Path	β	p-Value	Result
ESGA → BI	0.455	0.000	Supported
PE → BI	0.177	0.001	Supported
EE → BI	0.214	0.001	Supported
SI → BI	0.112	0.002	Supported
FC → BI	0.040	0.288	Not Supported
PE X ESGA → BI	0.185	0.000	Moderation Supported
EE X ESGA → BI	0.160	0.000	Moderation Supported
SI X ESGA → BI	0.140	0.000	Moderation Supported
FC X ESGA → BI	0.271	0.000	Moderation Supported
ESGA → AtGF	0.312	0.001	Supported
AtGF → BI	0.281	0.001	Supported

shows the hypothesis testing results. Regarding the direct influence on behavioral intentions to adopt Green FinTech, ESG awareness has the highest significant influence (ESGA → BI: β =0.455). Performance expectancy and effort expectancy have a lesser significant contribution to behavioral intention, with PE → BI: β = 0.177 and EE → BI: 0.225, respectively. Social influence shows a lesser impact on behavioral intention, with SI → BI: β = 0.112. Facilitating conditions, although positive, show an insignificant effect on behavioral intentions (FC → BI: β = 0.040); hence, they do not support H5. In addition, ESGA has a direct and significant impact on the individuals’ attitude towards Green FinTech (ESGA → AtGF: β = 0.312). Attitude towards Green FinTech exerts a positive and significant impact on BI (AtGF → BI: β = 0.281). These results are consistent with past studies where these constructs often weaken in voluntary use contexts. All f² values are greater than 0.02, which represent small to large effect sizes [67], showing meaningful relationships within the research model.

With regard to the moderation effects of ESG awareness, the results strongly support the proposed research model and provide important insights regarding the role of ESG awareness in impacting the consumer’s behavior. ESG awareness, as shown in Table 4, moderates all the proposed predictors of BI, which translates to the fact that individuals with higher awareness respond more favorably to motivational drivers. The moderating effect of awareness on PE (PE X ESGA → BI: β = 0.185, p < 0.001) shows that PE is more influential when ESG awareness is higher. For EE, the results show that EE X ESGA → BI: β = 0.160, p < 0.001. When it comes to SI, the moderating effect is also positive (SI X ESGA → BI: β = 0.140, p < 0.001). Interestingly, although the direct impact of facilitating conditions is contrary to what we expected and shows an insignificant relationship (FC → BI: β = 0.062, p = 0.052), the interaction between ESGA and FC reveals a strong positive and significant moderating effect (FC X ESGA → BI: β = 0.271, p < 0.001). The significant interaction between facilitating conditions and ESG awareness indicates that the influence of facilitating conditions on intention is contingent upon users’ level of ESG awareness. In addition, as shown in

Table 6. Simple slope analysis.

Path	Low ESGA	High ESGA	Conclusion
PE → BI	0.12	0.24	Stronger with ESG
EE → BI	0.04	0.18	Stronger with ESG
FC → BI	0.02	0.14	Stronger with ESG
SI → BI	0.01	0.07	Weak/moderate

, the simple slope analysis shows that ESG awareness significantly amplifies the relationships between PE, EE, FC, SI, and BI. In particular, the effects of PE, EE, and FC on BI are substantially stronger among individuals with high ESG awareness in comparison to individuals with low ESG awareness. This finding suggests that ESG-oriented cognition sharpens individuals’ sensitivity to Green FinTech and reinforces their adoption intentions. Finally, the sub-sample analysis shown in

Table 7. Sub-sample regression analysis.

Path	Original β	Sub-Sample βS1	Sub Sample βS2	Consistency
ESGA → BI	0.455 ***	0.441 ***	0.468 ***	Stable
PE → BI	0.177 **	0.169 **	0.186 **	Stable
EE → BI	0.214 **	0.203 **	0.226 **	Stable
SI → BI	0.112 **	0.104 *	0.121 **	Stable
FC → BI	0.040	0.035	0.046	Stable
PE X ESGA → BI	0.185 ***	0.174 ***	0.196 ***	Stable
EE X ESGA → BI	0.160 ***	0.149 ***	0.171 ***	Stable
SI X ESGA → BI	0.140 ***	0.131 ***	0.152 ***	Stable
FC X ESGA → BI	0.271 ***	0.258 ***	0.284 ***	Stable
ESGA → AtGF	0.312 **	0.298 **	0.326 **	Stable
AtGF → BI	0.281 **	0.269 **	0.294 **	Stable

*** p < 0.01, ** p < 0.05, * p < 0.1.

indicates that the structural relationships are consistent across male and female respondents, with all key paths maintaining their direction and statistical significance. This suggests that the proposed model is stable and not sensitive to sex differences.

6. Discussion

The results of our study, which were reported in the previous section, reveal that traditional technology acceptance predictors and the newly emerged environmental factors are both shaping the behavioral intentions of Gen Z consumers when it comes to adopting green banking applications and services. The results in this study are in line with the majority of the UTAUT theoretical assumptions. Such an outcome reinforces previous studies that demonstrated how users adopt digital technologies and services when they perceive them to be useful and easy to use [29,39,46]. In the context of green banking, the core constructs are important drivers, and most of them remain applicable, but the data analysis uncovered some noteworthy patterns worth examining. First, we found that when it comes to Gen Z, the direct effect of SI is not at the same level as the other predictors. However, in the presence of the ESGA, the effect increases, suggesting that Gen Z users are more likely to rely on their own attitudes, digital literacy, and sustainability values when evaluating green financial services, rather than on social pressure from peers or family. Regarding the FC, the results show unexpected outcomes. FC is not a significant predictor of Gen Z’s behavioral intentions, but in the presence of ESGA, the relationship becomes significant. This indicates full moderation due to the special nature of Gen Z users. Digital natives like Gen Z users act with more autonomy and can be less driven by the sole impact of facilitating conditions when it comes to digital technology adoption in comparison to digital immigrants [68,69].

In addition to showing that the UTAUT acceptance predictors remain salient factors in predicting users’ behavioral intentions, we unveil that environmental factors play an important role in shaping Gen Z users’ behavior when it comes to Green FinTech. These findings are in alignment with prior research in information systems and sustainability that showed how perceived environmental benefits have enhanced the adoption of environmentally friendly and green technologies [70,71]. The ESG and users’ awareness of its importance have amplified the impact of all of the UTAUT predictors and predicted attitudes. The results shed light on the importance and impact of ESG awareness in shaping how Gen Z consumers respond favorably to different motivators regarding FinTech. In addition, ESGA has a positive and significant impact on users’ attitudes towards green FinTech. Such demonstrate how ESGA would also increase adoption behavior indirectly through changing users’ attitudes towards FinTech. Interestingly, the study has also uncovered that even when the facilitating conditions do not significantly impact Gen Z users, ESG awareness effectively moderates that relationship so it becomes significant. Of the 11 hypotheses tested, 10 were supported by the PLS-SEM data analysis. The results show significant effects for PE, EE, SI, and AtGF on BI. The analysis also reveal a significant moderating effect for ESG awareness on young customers’ BI. Overall, our extended technology adoption model accounts for a substantial amount of the variance in behavioral intention, with an R² of 65%. This good explainability of the BI variance shows the importance of integrating ESG awareness into technology acceptance models, which has been raised as a research need in sustainability-focused research [8,9,10,11].

6.1. Theoretical Implications

This study contributes theoretically by extending UTAUT through the integration of ESG awareness as a value-driven cognitive mechanism. Unlike traditional predictors focused on functional utility, ESG awareness introduces a sustainability-oriented evaluation lens that reshapes how users interpret technological benefits. This dual role as both predictor and moderator differentiates the model from existing Green FinTech adoption frameworks. Further, our findings show that ESG awareness plays a more important role than previously assumed, influencing different important technology adoption drivers. In addition, the non-significant effects of facilitating conditions on Gen Z users challenge the universal assumptions of the UTAUT and reveal the significance of contextual and cultural factors in Green FinTech adoption research in understudied demographics.

6.2. Practical Implications

The results of this research provide important and actionable insights for decision makers in different financial institutions and governmental agencies that aim to improve the adoption of Green FinTech. While Gen Z consumers might be influenced by classical factors such as ease of use, performance expectations, and social influence, those factors may not alone influence their adoption behavior. ESG awareness strengthens the impact of other adoption drivers and boosts the effectiveness of institutional efforts to promote Green FinTech use. In addition, ESG awareness has a significant effect on changing the attitude of consumers towards green FinTech. That being said, policymakers and government regulators can leverage the findings of this study and formulate different campaigns to raise ESG awareness. This could be more applicable in countries like Saudi Arabia that have raised sustainability as a national goal, aiming to transition their economy from oil-dependent to diverse and sustainable.

7. Conclusions

In the past few years, Green FinTech has attracted growing attention from financial organizations seeking to embed environmentally responsible practices into their operations. In this study, we investigated how ESG awareness affects the behavioral intention of Gen Z consumers in Saudi Arabia and determines their adoption of Green FinTech. The theoretical foundation for this research was based on an extended UTAUT model.

The results indicate that ESG awareness is a strong predictor of consumers’ behavioral intentions regarding Green FinTech. This study established the importance of the UTAUT model in explaining customers’ intentions to use green FinTech, as well as the effect of ESG awareness and the attitude towards FinTech in shaping the consumer behavioral intention.

This study has a few limitations. First, it mainly focuses on young and educated customers, which may limit the generalizability of the findings. While this demographic aligns with this study’s focus on Generation Z, future studies could replicate this research in other cultures with a lower educational level among Gen Z users. Second, the study utilized cross-sectional and self-reported questionnaires to collect data; future studies could utilize different research methods to investigate and measure customers’ behaviors. Third, the study sample may have limited generalizability due to its composition. Future studies could replicate this research in other countries where digital services are limited.