Driving sustainability: how FinTech transforms SDG performance in SMEs

The term FinTech, originating in the early 1990s alongside the internet revolution (Haddad and Hornuf, 2019), has evolved into a critical driver of innovation in financial services. Over the past decade, rapid advancements in digital technologies have permeated all economic sectors, transforming traditional practices. The financial industry has been reshaped by this trend, with conventional “brick-and-mortar” banking increasingly replaced by digital-first models centered on advanced IT systems, data management and specialized human capital (Campanella et al., 2023). FinTech services are lauded for enhancing transparency, consumer engagement, cost-effectiveness and accessibility in financial services. Empirical evidence further highlights their potential to foster flexibility, efficiency, personalization, risk mitigation and inclusive growth, making them indispensable to modern economies.

The relevance of FinTech in achieving Sustainable Development Goals (SDGs) has emerged as a significant research area, particularly for small and medium enterprises (SMEs). As highlighted by the World Bank (2022), FinTech solutions play a crucial role in promoting financial inclusion, facilitating access to credit and enhancing transparency in sustainability reporting, key components for aligning SME operations with SDG targets. Moreover, emerging studies (e.g. Hernández et al., 2024; Mondal et al., 2024) underscore how digital finance and data-driven tools enable SMEs to monitor and optimize their environmental and social impact through cost-effective, scalable solutions. FinTech offers SMEs innovative solutions to improve financial inclusion, foster sustainable practices and measure SDG-related performance. These services enable cost-effective access to financing, digital payment systems and data-driven decision-making tools, crucial for integrating SDG performance metrics into business strategies (KRA and Bhat, 2024). The COVID-19 pandemic accelerated the growth of FinTech services, reshaping consumer and business behaviors (Nicolò et al., 2024). Digital payment systems and other FinTech solutions gained traction as businesses adapted to lockdowns, social distancing and shifting consumer preferences. Despite the growing interest in the academic literature, few studies have empirically investigated the factors that influence FinTech adoption among SMEs with a specific focus on sustainability performance metrics. This represents a clear research gap, particularly in country-specific contexts where SMEs face structural barriers to digital transformation and sustainability integration. Addressing this gap, this study focuses on the Italian context, which offers a compelling environment for investigating FinTech adoption among SMEs. Italy is characterized by a high prevalence of SMEs, which represent over 99% of all enterprises and play a central role in the country’s economic fabric. However, many of these firms face structural challenges, such as limited access to finance, digital skills gaps and bureaucratic complexity, which FinTech solutions are well-positioned to address. Moreover, Italy’s FinTech sector has experienced significant growth in recent years, spurred by regulatory developments (e.g. the PSD2 directive and national sandbox initiatives) and increasing interest from both traditional financial institutions and startups (Campanella et al., 2023). These factors make Italy an especially relevant case to explore how FinTech can support SMEs in achieving sustainability-related objectives.

This study aims to fill this lack in literature focusing on the factors influencing the adoption of FinTech services among some Italian SMEs and their role in enhancing SDG performance measurement. To this aim, the study employs the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the determinants of FinTech adoption in SMEs. By examining the role of performance expectancy, social influence, facilitating conditions, effort expectancy and experience, the research provides a holistic understanding of FinTech’s transformative impact on SDG performance in the Italian SMEs context. This integration of FinTech and sustainability offers valuable insights for policymakers, industry stakeholders and researchers seeking to bridge technology with sustainable business practices (Kurniawan et al., 2022).

Results show the existence of significant positive relationship between performance expectancy and social influence on the intention to adopt FinTech services confirming the importance of these theoretical construct as main predictors of the intention to adopt. It is worth to note that also facilitating conditions, adequate resources, infrastructure and support systems positively contribute to the adoption of FinTech technologies.

The study contributes to existing literature extending the UTAUT framework to the SME context, specifically focusing on sustainability. It highlights the role of social and external factors in driving FinTech adoption, as opposed to individual characteristics, and it provides practical implications for FinTech providers, policymakers and SMEs, emphasizing the need for support systems, training and user-friendly solutions.

The study has also practical implications. FinTech providers should find tailor solutions to SME needs, offer onboarding support and use social proof to build trust and encourage adoption. In addition, policy makers should create supportive ecosystems, provide financial incentives and ensure clear regulations to foster confidence in FinTech technologies. Finally, SMEs can focus on aligning FinTech adoption with strategic goals and leveraging tools for operational efficiency and sustainability reporting.

The remainder of this paper is structured as follows. Section 2 reviews the literature whereas Section 3 develops the hypotheses. Section 4 describes the research methodology. The results are presented and discussed in Section 5. Finally, Section 6 concludes and presents the implications of the study.

Nowadays, FinTech represents the fusion of technology with financial services, introducing innovative solutions that disrupt traditional financial practices. The features of FinTech are diverse and multifaceted, enabling improved efficiency, accessibility and customization of financial products and services. These features have played a pivotal role in reshaping the financial landscape, making services more inclusive and user-friendly.

One of the defining features of FinTech is its emphasis on digital accessibility, which ensures that financial services are available anytime and anywhere (Mhlanga, 2023). Traditional banking systems often require physical branch visits and are constrained by operating hours. In contrast, FinTech platforms leverage mobile applications, online portals and cloud computing to provide round-the-clock services. Digital accessibility extends beyond traditional banking services (Kou, 2019). Crowd funding platforms, for example, allow entrepreneurs to raise funds globally, whereas micro-lending apps provide small-scale loans to individuals without requiring credit history documentation. These innovations highlight how FinTech reduces entry barriers, fosters financial inclusion and expands service coverage to previously unbanked populations (Idrees and Ullah, 2024).

While these innovations benefit all types of users, they are particularly transformative for SMEs, which often face limited access to traditional financial services due to higher perceived credit risk, lack of collateral and complex bureaucratic requirements (Campanella et al., 2023). The adoption of FinTech solutions by SMEs plays a pivotal role in addressing these limitations, as they offer scalable, cost-effective tools such as digital credit scoring, user-friendly platforms and simplified onboarding procedures, making financial inclusion more attainable (Alassaf et al., 2024). However, SMEs face unique and more pronounced barriers in their digital transformation journeys compared to larger corporations (Karim et al., 2022). These include significant resource constraints, skills shortages, limited strategic guidance and underdeveloped IT infrastructures (Soni et al., 2022). FinTech, in this regard, serves as an enabler by reducing entry costs and democratizing access to digital capabilities, helping SMEs to bridge the digital divide.

Another critical feature of FinTech is data-driven personalization, which uses advanced analytics and artificial intelligence (AI) to tailor financial products to individual users’ needs. Traditional financial institutions often offer standardized services, but FinTech platforms capitalize on data to provide a more customized user experience.

Automation is at the core of FinTech’s transformative power. By automating routine financial processes, FinTech reduces costs, minimizes human errors and accelerates service delivery (Anifa et al., 2022). This feature has been instrumental in areas such as payment processing, loan underwriting and investment management. The integration of automation improves overall efficiency, enabling FinTech firms to scale rapidly and meet the growing demand for fast, reliable financial services (Tembo and Okoro, 2021). Importantly, SMEs are increasingly recognized as crucial players in advancing sustainability and technological innovation (Campanella et al., 2025; Biondi et al., 2002). Their agility and localized knowledge make them ideal candidates for experimenting with and scaling sustainable practices (Kuzma et al., 2020). When empowered with accessible digital technologies, SMEs can better monitor their environmental footprint, improve transparency and align operations with sustainability goals (Zhang et al., 2024). FinTech thus becomes a strategic ally in facilitating this transition, providing platforms for ESG reporting, sustainable finance and compliance with SDG-aligned metrics (Wu, 2017).

FinTech has the potential to revolutionize financial services by offering unprecedented benefits, from inclusivity and efficiency to transparency and personalization. By integrating innovative technologies into traditional financial services (Kou, 2019; Kou et al., 2024), FinTech has delivered numerous benefits, from increased accessibility to greater efficiency. However, this kind of technology has also potential issues that could reduce the degree of adoption by users. Both benefits and open issues are widely discussed in literature.

For the purpose of this research, we employ the UTAUT, widely employed to explain user intentions regarding the adoption of innovations and the subsequent usage behaviors (i.e. Ferri et al., 2021). It does so through five primary constructs: performance expectancy, effort expectancy, social influence, facilitating conditions and experience (Venkatesh et al., 2003), whereas previous authors found interesting insights about personal factors such as gender, age, experience and voluntariness that are proposed as mediators in the relationship between theoretical constructs and intention to use technology (Venkatesh et al., 2003; Nicolò et al., 2024).

This model has received significant recognition for its explanatory power. For instance, Waehama et al. (2014) highlighted its ability to account for over 70% of technology acceptance behavior, outperforming alternative models that typically explain less than 40%. Moreover, it is instrumental in assessing the acceptance of emerging technologies. However, UTAUT has also faced criticism for not addressing scenarios where core belief disconfirmation occurs, which could affect behavioral intentions and usage patterns (Venkatesh et al., 2012).

In the context of this study, performance expectancy is conceptualized as users’ perception of FinTech technology as a tool for enhancing productivity, streamlining operations and improving organizational efficiency (Castilla-Polo and Guerrero-Baena, 2023). FinTech is often associated with high expectations due to its potential to reduce complexity, enhance transparency and mitigate uncertainty in business processes. For example, its ability to securely manage transactions, provide immutable records and facilitate trust in distributed systems has positioned it as a transformative technology across various industries (Ding et al., 2022; Ediagbonya and Tioluwani, 2023). These characteristics are particularly appealing in environments where trust and efficiency are paramount (Iqbal et al., 2024).

Previous research has consistently shown that performance expectancy is a critical driver of behavioral intention to adopt and use new technologies (Benjamin et al., 2024). Studies have demonstrated that when individuals perceive a technology as capable of significantly improving their job performance, their intention to adopt and integrate the technology into their workflows increases substantially (KRA and Bhat, 2024). This relationship underscores the importance of framing technological tools in ways that emphasize their productivity-enhancing benefits to drive adoption.

In this study, we extend the concept of performance expectancy to examine its role in influencing SMEs’ intention to adopt FinTech services for enhancing SDG performance (Castilla-Polo and Guerrero-Baena, 2023). Indeed, this technology offers to SMEs new opportunities to optimize resource allocation, improve financial transparency and implement sustainable practices. By enhancing operational efficiency and supporting SDG-related initiatives, these services align closely with the expectations tied to performance benefits. Building on this theoretical and empirical foundation, we propose the following hypothesis:

Social influence (SI) examines how the opinions of an individual’s social circle impact their choices (Chen et al., 2022). It specifically focuses on the individual’s consideration of the beliefs held by people they regard as significant or influential (Venkatesh et al., 2003). This concept has been extensively studied in the context of technology adoption. For instance, prior research has explored its effects on adopting internet banking (Kazi and Adeel Mannan, 2013) and mobile government services (Rahi et al., 2018). These studies highlight the pivotal role of collaboration and social relationships in shaping individuals’ willingness to embrace new technologies (Venkatesh et al., 2003).

Notably, SI is particularly influential in structured, hierarchical environments, such as organizations or firms, where the opinions of evaluative authorities carry significant weight in decision-making. Several studies emphasize how individuals’ decisions are shaped by the views of their superiors (Chan et al., 2022; Singh et al., 2020). However, even within the flatter structures typical of SMEs, SI can play a crucial role. In such settings, decisions are often concentrated in the hands of a few key actors, owners, founders or external consultants, whose views and experiences exert a strong influence on innovation choices (Irimia-Diéguez et al., 2023; Chan et al., 2022). Furthermore, SMEs often rely on informal networks, including peer businesses, trade associations or financial advisors, making them particularly sensitive to social proof and external validation in the adoption of emerging technologies such as FinTech.

Despite the less formal hierarchy, social influence in SMEs can manifest through trust-based relationships, shared knowledge and external pressure to conform to perceived best practices, especially in highly regulated or competitive sectors.

In the context of emerging technologies, like FinTech, this influence becomes particularly relevant.

Based on this understanding, it is hypothesized that SI will play a significant role in shaping SMEs’ intentions to adopt FinTech services for SDG-driven strategies. Hence:

Facilitating conditions (FC) refer to the extent to which individuals believe that the necessary organizational and technical infrastructure is available to support the use of a system (Venkatesh et al., 2003, p. 453). In this study, FC are conceptualized in the context of SME awareness of the resources and support mechanisms available to facilitate the implementation of FinTech technologies. This includes access to training, technological infrastructure and advisory services that aid in overcoming the complexities associated with adopting innovative digital solutions (Venkatesh et al., 2003). Unlike large firms, SMEs often lack internal IT departments or dedicated innovation units, making them more dependent on external support systems to adopt and integrate new technologies. As a result, facilitating conditions have an amplified role in the SME context; they not only enhance perceived ease of use but also shape the feasibility and strategic fit of FinTech solutions.

For instance, Oliveira et al. (2016) and Zhou and Li (2014) demonstrate that facilitating conditions mitigate the inefficiencies and uncertainties typically associated with technological transitions. Alalwan et al. (2018) further argued that when users perceive the availability of adequate infrastructure and training, they are more likely to adopt and fully integrate these technologies into their operational processes. Moreover, public policy and institutional frameworks play a complementary role in enhancing facilitating conditions for SMEs (Abu et al., 2024). National initiatives such as regulatory sandboxes, innovation hubs and digital vouchers or incentives can create an environment conducive to digital experimentation (Verma et al., 2023) without significant upfront investment, making the adoption of FinTech both more accessible and strategically viable.

Given the significant role of facilitating conditions and building on the insights from previous research, we propose that the presence of resources and infrastructure designed to support SMEs in adopting FinTech services will positively influence their intention to embrace these technologies. Based on this understanding, we present the following hypothesis:

Effort expectancy (EE), as conceptualized within the UTAUT framework, refers to the perceived ease of using an information system (Venkatesh et al., 2012). This construct encapsulates the effort users anticipate they must expend to learn, adopt and effectively employ a new system. High levels of EE imply a system’s usability aligns with user expectations, thereby reducing psychological barriers to adoption. Research underscores that when users perceive a new technology as requiring minimal effort to integrate into their daily activities, their intention to adopt that technology significantly increases (Oliveira et al., 2016). This dynamic is especially relevant for SMEs, which often lack dedicated IT staff or advanced digital competencies. Unlike large corporations, SMEs may not have the internal expertise or financial capacity to manage complex digital transitions, making the perceived ease of use a critical determinant of adoption (Kajol et al., 2022). As a result, technologies that require minimal training and are intuitive to implement are more likely to be adopted in SME contexts.

Furthermore, given the lean organizational structure of SMEs, where decision-makers are often involved in day-to-day operations, time-saving and user-friendly technologies become essential enablers. In this regard, FinTech solutions that minimize learning curves and operational disruption are more appealing and feasible for small firms aiming to enhance SDG performance metrics.

In light of these considerations, the hypothesized relationship between EE and SMEs’ intention to integrate FinTech services is grounded in both theoretical and practical imperatives. From a theoretical perspective, EE aligns with core tenets of UTAUT, reinforcing its predictive power in understanding technology adoption.

Furthermore, the hypothesis links EE to SDGs, highlighting a critical intersection between technology adoption and organizational commitment to sustainability. Hence:

Experience (EXP) with digital solutions reflects an organization’s accumulated knowledge and practical interactions with technology (Kou, 2019). This experience may range from basic digital tools (e.g. accounting software, CRM systems) to more complex platforms (e.g. data analytics and AI-driven tools). Drawing from UTAUT, prior experience with digital technologies can significantly lower perceived barriers to adoption, such as effort expectancy, while enhancing facilitating conditions and performance expectancy.

For SMEs, digital experience is particularly crucial due to their typically limited resources and capacity for experimentation. Firms with a history of successfully implementing and leveraging digital solutions are better positioned to perceive FinTech adoption as a natural progression rather than a disruptive or risky shift (Karim et al., 2022). Moreover, prior digital experience may reduce implementation risks, lower resistance to change and enhance the perceived feasibility of integrating new tools, especially when resource constraints are a limiting factor (Kessler et al., 2022).

Furthermore, prior experience with digital tools can act as a risk mitigation mechanism. It helps SMEs anticipate potential implementation challenges, optimize resource allocation and minimize operational disruptions during technology integration (Yang and Yee, 2022).

This is particularly relevant in the context of SDG-related innovations, where aligning financial tools with sustainability goals may otherwise appear too complex or abstract for resource-constrained SMEs. In this sense, digital experience fosters a more favorable perception of feasibility, reduces resistance to change and enhances internal readiness, ultimately strengthening the intention to adopt FinTech solutions aligned with SDGs. On this basis, we formulate the following hypothesis:

To identify whether any significant personal factors influence the intention to introduce FinTech technology in SMEs, we incorporated three control variables: age, gender and professional role within the firm.

With reference to age, it may impact the intention to use FinTech technology, as older individuals are often more resistant to adopting technological innovations (Choi et al., 2024). Similarly, gender has been found in various studies to influence an individual’s intention to adopt new technology (Choi et al., 2024). Finally, the role within the firm is included as a control variable, as individuals in higher hierarchical positions are often not mandated to use new technologies (Ferri et al., 2018 and 2021). All hypotheses and the effect on intention are summarized in Figure 1.

The study employed a structured questionnaire based on a six-point Likert scale to collect quantitative data. In line with previous research (e.g. Ferri et al., 2021), the instrument included 28 items corresponding to six theoretical constructs derived from the UTAUT model. An initial section of the questionnaire was dedicated to collecting information on control variables.

To reduce the potential for “central tendency bias”, an even-numbered Likert scale ranging from 1 (minimum) to 6 (maximum) was adopted. All measurement items were adapted from validated scales in the literature and modified to fit the specific context of FinTech adoption by SMEs.

Although the questionnaire addressed the SDGs as a comprehensive agenda rather than targeting individual goals, the discussion connects each construct with specific SDGs based on their theoretical implications and alignment with international frameworks (UN, 2015). For example, performance expectancy is interpreted in relation to SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation and Infrastructure), as these goals emphasize innovation-driven efficiency and competitiveness. Social influence is linked to SDG 17 (Partnerships for the Goals), highlighting the importance of network dynamics and collaboration in achieving sustainability. These associations were not empirically tested per goal but rather reflect a conceptual alignment based on the literature and observed patterns in SMEs’ FinTech adoption behavior.

A pilot study was conducted prior to the full-scale data collection. A group of 49 graduate students and researchers voluntarily completed the questionnaire. Based on their feedback, minor revisions were introduced to enhance clarity and readability, such as the replacement of uncommon or overly technical terms, while ensuring the content validity of the constructs remained intact.

After the pilot phase, the questionnaire was distributed to Italian SMEs (see  Appendix). Specifically, we have identified through Bureau van Dijk the all the Italian Innovative SMEs (n. 2,197). We selected Innovative SMEs because are more likely to adopt digital tools, like FinTech. For each identified firm, we retrieved the contact information of the Chief Executive Officer (CEO), primarily through LinkedIn, to facilitate direct outreach. The data collection process took place over a three-month period, from September to November 2022.

A total of 332 responses were collected during this period. After removing incomplete entries and excluding 18 respondents who indicated that they were not FinTech users, 305 valid responses remained for analysis. The characteristics of the research sample are shown in Table 1.

In line with previous research, we evaluated the scale reliability using principal component analysis with varimax rotation and Kaiser normalization. Bartlett’s test of sphericity yielded a significance level of 0.000, and the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.843. Because a KMO value above 0.5 is considered acceptable for ensuring sampling adequacy and data factorability, our results indicate strong validity. Variables were grouped into four factors, collectively accounting for 78.01% of the total variance. No items were dropped. This suggests that the extracted factors effectively explain a significant portion of SMEs’ intentions to adopt FinTech technology. In addition, we assessed the reliability of the survey using Cronbach’s alpha. Table 2 presents the detailed results.

Previous Table 2 shows that Cronbach’s alpha results are statistically reliable (because higher than 0.7) and similar with those of previous authors (Gangwar et al., 2015) with all value ranging between 0.819 and 0.918.

To evaluate the model’s goodness-of-fit, we employed several indices. First, we used the chi-square test to assess the model’s ability to capture the variance and covariance of the data, as recommended in prior studies (Byrne, 2013). The test yielded a chi-square value divided by the degrees of freedom of 0.641 (p < 0.001). It is important to note that the chi-square test is particularly sensitive to sample size. To address this limitation, and in line with previous research, we incorporated additional fit indices for a more comprehensive assessment. Table 3 presents the fit indices, their results, and the corresponding reference values.

Also, we carried out a test for the average block VIF (AVIF = 1.358, acceptable if <= 5, ideally <= 3.3), average full collinearity VIF (AFVIF = 1.231, acceptable if <= 5, ideally <= 3.3) and Simpson’s paradox ratio (SPR = 1, acceptable if >= 0.7, ideally = 1).

After the measurement of model’s goodness-of-fit, we carried out structural equation modeling to understand the effect of each latent variable on the SMEs’ intention to use FinTech technology. Table 4 presents the research results.

The explanatory power of our model was tested using the average R-square. Our model explains the 36.2% of the total variance. Looking at the research hypothesis it is interesting to note that our results are in line with the expectations. With reference to performance expectancy (PE), our results reveal a significant and positive effect on SMEs’ intention to adopt FinTech technology (coeff. 0.32, p < 0.01).

With reference to effort expectancy (EE), our findings indicate a positive effect on INT (coeff. 0.14 with p < 0.01), leading to the acceptance of H2. This result suggests that when FinTech solutions are perceived as relatively easy to learn and integrate, SMEs are more willing to adopt them.

With reference to SI, our results show the existence of a strong positive effect on SMEs’ intention to adopt FinTech (coeff. 0.27 with p < 0.01). This result suggests that SMEs are significantly influenced by the social environment in their decision-making processes regarding FinTech adoption.

Our results supports HP3, which posited that social influence would have a positive impact on SMEs’ intention to adopt FinTech. The strength of the effect observed in this study (coeff. 0.27 with p < 0.01) not only validates H3 but also reinforces the argument that interventions aimed at promoting FinTech adoption should focus on leveraging social influence. With reference to H4, our results provide robust support for the hypothesis that FC have a positive impact on SMEs’ intention to adopt FinTech technology. The statistical analysis reveals a coefficient of 0.16, which is significant at the p < 0.01 level, indicating a strong and meaningful relationship. This finding underscores the critical role of facilitating conditions – such as the availability of technological infrastructure, user training and technical support – in shaping SMEs’ willingness and ability to integrate FinTech solutions into their operations.

The hypothesis HP5 (coeff = 0.13) posits that experience (EXP) positively moderates SMEs’ intention to adopt FinTech services for advancing SDG-related objectives. The assumption of a positive moderation effect aligns with the notion that prior exposure to and familiarity with digital technologies creates a foundation of confidence, competence and readiness within SMEs, making them more likely to embrace FinTech solutions as part of their sustainability initiatives. Finally, with reference to the control variables, our findings provide nuanced insights into the factors influencing SMEs’ intention to adopt FinTech technology. We observed that age and role within the firm exhibit a low but statistically significant negative effect on the intention to use FinTech (coeff. –0.05 with p < 0.01 and coeff. –0.09 with p < 0.01, respectively). This indicates that younger respondents are more inclined to adopt FinTech compared to their older counterparts. In addition, the negative coefficient for role in the firm suggests that individuals in more senior positions, who might have less hands-on interaction with technology or greater attachment to traditional processes, may exhibit lower enthusiasm for adopting FinTech solutions.

In contrast, gender appears to have no statistically significant impact on SMEs’ intention to use FinTech (coeff. 0.00 with p < 0.10).

Our findings highlights the critical role of PE as a determinant of behavioral intention, highlighting how the anticipated benefits of FinTech adoption drive the willingness of SMEs to embrace these innovations. Specifically, the data suggest that as expectations regarding the performance enhancements brought by FinTech increase, so does the likelihood of individuals and organizations adopting such technologies. This insight has broader implications for SMEs, particularly in the context of SDGs. We argue SMEs anticipate substantial improvements in their operational efficiency, financial management and overall performance as a result of implementing FinTech-based practices. Such advancements align with SDG priorities, including fostering innovation (SDG 9), promoting economic growth (SDG 8) and enhancing financial inclusivity (SDG 1 and SDG 10). By integrating FinTech solutions, SMEs not only stand to achieve direct performance benefits but also contribute to broader societal and environmental objectives (Rafiq et al., 2024).

However, while PE emerges as a strong predictor, its discussion must be balanced with insights from other constructs, which also reveal key adoption dynamics. For example, EE proved to be a barrier, particularly among SMEs with limited digital maturity. The perceived complexity of FinTech tools, especially their misalignment with existing workflows, fosters skepticism and resistance. This is consistent with findings from Bierstaker et al. (2014), and especially relevant in the context of Italian SMEs, which often exhibit a low digital readiness and reluctance to change (Campanella et al., 2023). To mitigate these concerns, strategies such as phased implementation, simplified user interfaces and targeted capacity-building could be adopted.

This is particularly relevant in resource-constrained environments (Iqbal et al., 2024), where FinTech adoption can bridge gaps in access to traditional financial and technological services, enabling SMEs to achieve their goals more effectively. It is worth to note that the strong effect of PE on intention to adopt FinTech suggests that SMEs perceive significant efficiency and effectiveness gains, driven by automation and standardization of activities. The strong association between PE and adoption intention suggests practical strategies for policymakers, technology developers and SMEs’ stakeholders. To maximize adoption rates, these entities should focus on increasing awareness of FinTech’s potential benefits, providing training to enhance user competence and demonstrating tangible performance outcomes. In addition, tailored communication strategies emphasizing FinTech’s role in achieving SDGs could further motivate SMEs to integrate these technologies into their practices.

We argue FinTech is perceived by SMEs as difficult to implement and align with their current operational processes. This perception is particularly relevant for SMEs that see FinTech as requiring significant effort to re-master established procedures, develop new processes and adapt to novel logics of control. Such findings align with the observations of Bierstaker et al. (2014), who highlighted that SMEs tend to associate new technologies with high effort expectancy, reflecting a combination of apprehension and the anticipated workload involved in adapting to change.

This technological skepticism and perceived difficulty in use are particularly pronounced in the context of Italian SMEs (Campanella et al., 2023), which have historically demonstrated aversion to technological change. This resistance is often attributed to structural characteristics of SMEs, including limited resources, the absence of dedicated IT expertise and deeply ingrained traditional practices. As a result, the adoption of innovative tools like FinTech often encounters barriers not only in technical implementation but also in organizational mindset.

Despite these challenges, the current paper sheds light on an important avenue for future research: exploring strategies to reduce effort expectancy and overcome resistance to change in SMEs. This inquiry is crucial to fully unlocking the potential of FinTech for improving SDG performance in these enterprises. By addressing the psychological and operational hurdles associated with effort expectancy, scholars and practitioners can identify pathways to foster a more seamless and enthusiastic adoption of FinTech. For instance, targeted training programs, user-friendly interfaces and incremental implementation approaches could significantly alleviate perceived difficulties.

Furthermore, the findings emphasize the need for broader policy initiatives and support mechanisms aimed at enhancing SMEs’ technological readiness (Yan et al., 2021).

We found SMEs’ decisions to adopt FinTech technologies are strongly shaped by social dynamics and external influences. Specifically, SMEs are more likely to adopt FinTech solutions when there is a high level of acceptance and endorsement from relevant social groups, such as other auditors, customers and accountants. This highlights the critical role of social dynamics and peer influence in shaping organizational behavior (Rafiq et al., 2024). In parallel, SI significantly shapes SMEs’ FinTech adoption behavior. When trusted stakeholders, such as accountants, auditors, or peer entrepreneurs, validate FinTech solutions, SMEs are more likely to overcome doubts and commit to integration. This underscores the power of peer dynamics, network effects and reputation within SME ecosystems. Our findings are consistent with a growing body of literature that identifies SI as a key driver of technology adoption, particularly in trust-dependent settings.

Several studies (e.g. Anifa et al., 2022; Singh et al., 2020) have shown that the relational dimension, including perceptions of social approval and pressure, is often one of the strongest predictors of technology adoption in SMEs. This is particularly relevant in the context of FinTech, where trust and validation from external stakeholders can reduce perceived risks and uncertainty, thereby accelerating the adoption process. SI plays a major role in technology acceptance, aligning with research in other fields (Bierstaker et al., 2014) and confirming that social dynamics heavily influence the intention to embrace new technologies. Our results are consistent with research that underscores the pivotal roles of expected performance and social factors in the adoption of new technologies (Venkatesh et al., 2003, 2012; Wang et al., 2021). Moreover, this finding supports practical approaches might include targeted campaigns showcasing testimonials from early adopters, endorsements from trusted industry leaders and collaborative initiatives that involve multiple stakeholders within SMEs’ networks.

Facilitating conditions also play a vital role, especially given the typical resource constraints of SMEs with broader implications for advancing the SDGs (Rafiq et al., 2024).

Access to infrastructure, advisory support and financial incentives can substantially reduce the perceived risks and logistical burdens of FinTech adoption. This finding reinforces the importance of a supportive ecosystem, where both public and private stakeholders provide tangible enablers such as training, grants and shared technological platforms (Zhou and Li, 2014; Alalwan et al., 2018).

We argue experience enhances the relationship between SMEs’ intention to adopt FinTech and their commitment to SDG-related objectives. Familiarity with digital technologies builds internal capability and confidence, positioning SMEs to more readily incorporate FinTech into their sustainability efforts. Indeed, experience with digital solutions can enhance SMEs’ ability to navigate the complexities of FinTech platforms. Familiarity with digital tools reduces perceived risks and uncertainties, which are often significant barriers to technology adoption in resource-constrained organizations. Moreover, experienced SMEs are likely to have already invested in complementary infrastructures, such as reliable IT systems, and cultivated a workforce skilled in digital operations. This cumulative effect of prior experience can amplify the perceived value and practicality of adopting FinTech technologies to achieve SDG-related objectives, such as financial inclusion, energy efficiency and resource optimization. We found there is a higher inclination among younger respondents to adopt FinTech solutions when compared to older individuals. This result aligns with broader literature suggesting that younger professionals tend to have higher levels of technological adaptability and openness to innovation, likely due to greater exposure to digital tools in their education and early career stages.

In summary, our findings confirm that performance expectancy and social influence are the strongest predictors of FinTech adoption in SMEs, offering robust support for the UTAUT framework in this context. Facilitating conditions and digital experience also emerge as enablers, whereas effort expectancy presents a notable barrier, particularly for more traditional SMEs. Demographic factors such as age show limited but noteworthy influence, whereas others (e.g. gender) appear statistically insignificant. These insights contribute to both theoretical understanding and practical strategies aimed at promoting FinTech adoption in SMEs, with a view toward supporting their contribution to the SDGs.

This study investigates the adoption of FinTech by SMEs using the UTAUT. Our findings highlight that SI and PE are the main predictors of FinTech adoption, whereas personal factors, such as the respondent’s age, gender and experience, have a statistically insignificant effect on the decision to use FinTech solutions (KRA and Bhat, 2024).

Our results have implications for FinTech providers and policymakers. With reference to FinTech providers, our results show the significant role of social influence suggesting that providers should leverage networks and communities to promote their solutions. Building partnerships with industry associations, SME-focused organizations and trusted advisors can amplify the credibility of their offerings. Encouraging satisfied SME clients to act as advocates or participate in testimonial campaigns can also reinforce trust and drive adoption. Second, FinTech providers should recognize that personal factors such as the demographics of SME decision-makers have limited impact on adoption.

Policymakers have a critical role in fostering FinTech adoption among SMEs by creating an ecosystem that reduces barriers and builds trust. One effective strategy is to promote peer networking and knowledge sharing through industry forums, workshops and events. These platforms allow SMEs to learn from the experiences of peers who have successfully adopted FinTech solutions, making the benefits and processes more tangible. In addition, providing financial incentives, such as grants, tax relief or subsidies, can encourage SMEs to explore FinTech tools. Policymakers can also offer technical support and educational resources to help SMEs navigate the adoption process.

Our research points to the need for coordinated strategies that integrate private innovation with public support mechanisms to foster FinTech diffusion among SMEs. A joint approach is crucial; indeed, FinTech providers and policymakers must collaborate to reduce adoption barriers and enhance SMEs’ confidence in digital solutions. For example, FinTech providers can amplify the power of social influence by actively engaging with SME communities and professional networks. Partnering with industry associations and leveraging satisfied clients as brand ambassadors or case studies can significantly boost perceived trust and credibility.

At the same time, public actors should design policies that reinforce and complement private sector initiatives. This includes offering financial incentives (e.g. grants, tax credits), technical assistance and training programs tailored to the specific needs of SMEs. Moreover, governments can contribute to standardizing best practices and regulatory clarity, reducing perceived complexity and risk, particularly important in the context of integrating FinTech tools into sustainability strategies aligned with the SDGs.

Ultimately, a coordinated public–private effort can create a supportive innovation ecosystem that aligns the goals of all stakeholders. FinTech providers gain a broader and more trusting client base, policymakers support economic resilience and digital transformation and SMEs benefit from tools that enhance operational efficiency, sustainability measurement and long-term competitiveness.

In conclusion, by concentrating on tools that offer measurable performance improvements, SMEs can achieve greater operational efficiency. Leveraging social proof is another powerful approach; engaging with peers or businesses within the same industry that have successfully adopted FinTech can provide valuable insights and reduce uncertainty.

Despite its contributions, the study has several limitations. First, our research focuses on a sample of Italian SMEs which may not represent the diversity of the global SME ecosystem. Future studies should explore FinTech adoption in broader and more diverse settings. Second, the use of a cross-sectional survey limits the ability to observe changes in adoption behavior over time. Longitudinal studies could provide deeper insights into how FinTech adoption evolves in response to technological advancements and market changes. Finally, while the UTAUT model was effective, additional organizational and environmental factors, such as financial constraints or regulatory influences, could further enrich the understanding of FinTech adoption drivers. Then, we acknowledge that the decision to target innovative SMEs may introduce a selection bias, as these firms are typically more inclined to adopt new technologies and may have more digital-savvy personnel, such as innovation managers. This strategic choice was driven by the study’s focus on FinTech adoption and its alignment with digital transformation and sustainability goals, which are more likely to be present in such organizations.

However, this specificity represents both a limitation and a strength. On one hand, the sample may over-represent early adopters, potentially inflating intention-related constructs. On the other hand, it offers a relevant empirical context to explore the intersection between FinTech adoption and sustainability metrics, particularly within firms that are already engaged in innovation pathways. We also controlled for the presence of innovation-related roles (e.g. innovation managers) in our analysis to assess whether these profiles significantly influenced responses. Future research may benefit from comparative studies involving both traditional and innovative SMEs to further explore contextual differences in adoption behavior.

By addressing these limitations and building on our findings, future research can develop more comprehensive models and frameworks, further advancing the understanding of FinTech adoption in SMEs.