Fintech Adoption and Banks’ Non-Financial Performance: Do Circular Economy Practices Matter?

Abstract

This study draws insights from practice-based view theory (PBV) to investigate the impact of fintech adoption (FA) on the non-financial performance (NFP) of banking institutions in developing countries, considering the mediating role of circular economy practices (CEPs). A structured questionnaire was distributed to collect primary data from banks’ staff in Iraq, Egypt, Oman, and Jordan using a convenience sampling method with a sample size of 397. Subsequently, the structural equation model was utilized to test the research hypotheses of the proposed conceptual model. The study’s findings revealed that FA positively and significantly impacts CEPs and banks’ NFP (customer satisfaction, internal processes, and learning and growth perspectives). Moreover, CEPs mediate the relationship between FA and banks’ NFP in a positive and significant way. Given the dearth of the literature, this is the first study to fill the research gaps by investigating the impact of FA on the NFP of banking institutions in developing countries, considering CEPs as a mediator, and yielding critical theoretical and practical implications. The study’s findings provide banks’ managers with valuable insights about how to enhance their NFP through FA and CEPs during and after crises and support policymakers and regulators in developing a legislative framework that guides banks to invest in CE models and provides reward systems to encourage them.

1. Introduction

Although technology has been considered a part of the financial services industry since the 1850s, it has been developed rapidly only during the last two decades, which impacts the way of doing business, providing financial products and services, and serving customers (Murinde et al. 2022; Basdekis et al. 2022; Alkhazaleh and Haddad 2021). The financial services industry has changed rapidly, resulting in the development of new and innovative financial services called “Fintech” (Zhao et al. 2019; El-Said 2023).

Fintech has received an increasing interest in the financial business field. It is an abbreviation for “financial technology”, which is defined by the Financial Stability Board (FSB 2017) as new emerging technologies that promote new business models and technology applications with a significant impact on financial institutions. Al-Shari and Lokhande (2023); Gomber et al. (2018); and Vergara and Agudo (2021) referred to fintech as a fintech revolution because of its connection to emerging digital technologies like cloud computing (CC), big data (BD), the internet of things (IOT), blockchain, and artificial intelligence (AI), its integration into financial business, the emergence of fintech firms, and the provision of their digital financial services that attract new customers and banks to adopt and use them. Some of the financial services provided are mobile payment, crowdfunding, investment advice (robo-advising), insurance, and wealth management.

International Monetary Fund (IMF) and World Bank Group (2019) stated that fintech creates new opportunities and challenges for the financial sector, particularly the banking sector. According to Basdekis et al. (2022), the global financial crisis of 2008 and the intensified regulation of the banking sector proved to be the major reasons for fintech development. Consequently, fintech startups have expanded rapidly in the financial sector, which provide new and innovative financial services to customers that banks were not used to provide, because banks worldwide still offer traditional and costly financial services due to restricted regulations (Basdekis et al. 2022; El-Said 2023). As a result, fintech firms have the chance to provide traditional banks’ functions focusing on advanced technology to achieve a competitive advantage (Al-Shari and Lokhande 2023).

Although the spread of the COVID-19 pandemic results in instability and uncertainty in economies around the world, fintech has expanded rapidly, particularly in developing countries (Guang-Wen and Siddik 2023). Due to the digital revolution during the COVID-19 pandemic, physical communication between customers and financial services providers has been reduced, resulting in the adoption of fintech services like mobile payments and online banking (Baker et al. 2023) that are still effective and useful after the pandemic (Sharifi et al. 2021).

Due to the emergence of fintech and its importance in the financial service industry, there is an increasing interest in investigating its impact on the banking industry in detail. Paulet and Mavoori (2019) and Phan et al. (2020) stated that the growth of fintech firms has negative impacts on banks’ performance because of the possibility of replacing traditional banks in the future. Others stated that the rise of fintech leads to the birth of a new era for banking institutions because it improves the flexibility and functionality of banking operations (El-Said 2023); achieves service delivery, efficiency, effectiveness, and customer satisfaction; and lowers transaction costs in a significant way (Dwivedi et al. 2021; Guang-Wen and Siddik 2023).

Due to the significant role of the banking industry in economic development and the achievement of a country’s sustainable development, banks should adapt to advanced fintech to provide new banking products and services and achieve a sustainable competitive advantage (Yan et al. 2023; Dwivedi et al. 2021). Due to the importance of banks in the financial system, many researchers have focused on exploring the impacts of fintech adoption (FA) on banks’ performance (Chen et al. 2021; Murinde et al. 2022; Phan et al. 2020). Fintech adoption allows the banks to transform their traditional business models into a technologically disruptive one by adopting emerging technologies into their internal processes (Kharrat et al. 2023), which results in enhanced customer interactions, decision-making processes, and the development of new business models (Paulet and Mavoori 2019). On the other hand, many studies have empirically investigated fintech adoption’s impact on banks’ financial performance. Ky et al. (2019) stated that fintech adoption has positive impacts on banks’ profitability. Also, Zhao et al. (2019) and Baker et al. (2023) demonstrated that higher investment in fintech enhances banks’ financial performance. Studies by Yan et al. (2023) and Omarini (2018) have investigated the impact of adopting some banking technologies on banks’ financial performance by using financial measures like return on assets (ROA), return on equity (ROE), and other traditional financial measures considered in the banking literature.

From the previous studies, it can be noted that the existing research has not adequately explored the impact of FA on banks’ performance. The prior literature provided a descriptive analysis of potential opportunities and threats of fintech in the banking sector (Murinde et al. 2022; Al-Shari and Lokhande 2023; El-Said 2023) and others investigated the relationship between some technologies and banks’ financial performance (Baker et al. 2023; Liu et al. 2021; Yan et al. 2023; Omarini 2018). Despite several studies on the positive impacts of FA on banks’ financial performance, there is a dearth of the literature on FA on banks’ non-financial performance (NFP), and this is the first research gap in the existing literature. This study focuses on exploring the impact of FA on banks’ NFP due to the importance of NFP to the banks’ performance as mentioned by Chen et al. (2021) and Feng and Goli (2023): first, it supports the connection between the bank’s strategy and daily tasks between customers and employees; second, investigating NFP is a better predictor of banks’ long-term performance and supports managers monitoring and assessing their progress toward strategic goals; and third, the banking sector faces severe competition from fintech firms, and in such circumstances, focusing only on financial measures leads to a dismal picture for the banks; and fourth, NFP provides leading insights about the potential risks or opportunities which do not appear in the financial statements.

In addition, some scholars recommended an investigation of diverse moderators’ roles in the linkage between FA and banks’ performance. Some authors investigated the mediating role of access to finance (Siddik et al. 2023b); financial literacy (Lontchi et al. 2023); and green finance and green innovation (Yan et al. 2022; Guang-Wen and Siddik 2023). Only the study of Siddik et al. (2023a) explored the mediating role of circular economy practices (CEPs) on the association between FA and the sustainability performance of small- and medium-sized enterprises (SMEs). To our knowledge, no study investigated the mediating role of CEPs on the relationship between FA and banks’ NFP. So, this study investigates how CEPs mediate the relationship between FA and banks’ NFP to address the second research gap.

Through adopting CEPs, banks can promote the recycling of various types of waste generated; place recycling containers to collect recyclable items such as glass, paper, metals, and plastics; and dispose of broken and used equipment ethically and responsibly (Bukhari et al. 2020). Also, Feng and Goli (2023) stated that the essence of the CE is focusing on the efficient utilization of resources, which can be achieved through adopting recycling, reducing, and reusing practices. So, it is critical for banks to consider CE models due to three reasons: First, there is a significant role of banks in growing developed and developing countries’ economies. So, their adoption of CE models will support the transition towards CE growth. Second, the CE is a critical tool to reduce natural resource usage, waste generation, and environmental degradation. Third, banks can adopt CEPs to offer new financial models and credit lines for circular businesses; identify, select, and finance circular projects; create green banks; and promote waste reduction and reusing or recycling practices (Ali et al. 2022). However, there is a dearth of the literature on how FA facilitates banks’ transition into CE models to enhance their performance. Therefore, it is relevant to consider CEPs because they can guide banks’ managers on how to use financial resources efficiently to enhance their NFP, which would make a significant contribution to the fintech and banking domains.

Consequently, to fill the aforementioned research gaps, this study establishes a conceptual model to investigate the impact of FA and banks’ NFP in developing countries and explore the mediating role of CEPs, with the ability to test this model empirically by using the structural equation model (SEM). To attain the research objectives of this study, the following research questions will be addressed:

RQ1.

Does FA impact the NFP of banking institutions in Egypt, Iraq, Oman, and Jordan?

RQ2.

Do CEPs mediate the relationship between FA and the NFP of banking institutions in Egypt, Iraq, Oman, and Jordan?

According to Siddik et al. (2023a), this study focuses on a practice-based view (PBV) to explore the role of FA and CEPs in improving the NFP of banking institutions in developing countries. The PBV offers a theoretical lens for assessing the banks’ NFP because it offers insights into how imitable and transferable practices can enhance a firm’s performance. It can be noted that technological practices like fintech and CEP can be imitable and transferable from one organization to another and can be used to enhance a firm’s performance.

This paper is timely because there is a lack of understanding about the performance consequences of FA at the banking level, particularly in developing countries. The banking sector in developing countries has adopted fintech solutions rapidly, particularly during the COVID-19 era due to some reasons mentioned by Al-Shari and Lokhande (2023), Guang-Wen and Siddik (2023), and Murinde et al. (2022). First, there is a lack of traditional financial infrastructure due to the geographical characteristics of some areas, which makes banks adopt fintech to help people access financial services more easily and with lower costs. Second, most mobile phone users are young people, which is considered an opportunity for banks to provide financial services through FA. Third, most of the previous studies on FA were conducted in developed countries (Coetzee 2019; Phan et al. 2020; Kemunto and Kagiri 2018), ignoring developing countries. Therefore, the high bank adoption rate of fintech in developing countries is a critical motivation for conducting this study.

This study contributes to FA and the banks’ NFP literature in several ways. First, this study draws insights from PBV theory. Thus, this study contributes to the theoretical development of PBV theory concerning FA and how it enhances banks’ NFP in terms of CEPs as a mediator. Second, most of the fintech literature is reviews and case studies. So, in response to Pizzi et al. (2021) and Dasilas and Karanović’s (2023) calls to explore FA’s impact on banks’ performance through primary data-based empirical analysis, this study fills this gap in the FA literature by providing empirical evidence on the role of FA in facilitating banks’ transition into the CE model and consequently enhancing their NFP. Third, this study adds to the literature on the interconnections between FA, CEPs, and NFP. To the best of our knowledge, this is the first attempt to incorporate these variables into a single research model in the context of developing countries’ banking institutions. Fourth, it is the first study to investigate CEPs’ ability to mediate the relationship between FA and banks’ NFP, which adds to the CE literature. Fifth, to the best of the authors’ knowledge, no prior study has empirically explored the impact of FA on banks’ NFP in terms of CEPs as a mediator by using SEM. Sixth, the study’s findings will provide banks’ executives in developing countries with valuable mechanisms and ideas to enhance their NFP through FA and CEPs during and after crises like the COVID-19 pandemic.

The rest of the paper is organized as follows: the literature review and hypotheses’ development are presented in Section 2; Section 3 presents the research design, sample selection, data collection, and survey instrument development; Section 4 outlines the findings of the measurement and structural analyses, followed by a discussion in Section 5; and Section 6 presents the conclusion, research implications, limitations, and paths of future research.

2. Literature Review and Hypotheses Development

This section highlights the building blocks that support our investigation, providing a comprehensive exploration of fundamental concepts. The aim of reviewing the literature is to outline the connection between FA and banks’ NFP, considering PBV theory. It starts with a general background about PBV theory, FA, and the relationship between FA and the perspectives of banks’ NFP. Finally, the mediating role of CEPs in the linkage between FA and banks’ NFP is considered.

2.1. Practice-Based View (PBV)

PBV theory demonstrates the influence of imitable organizational practices on firms’ performance. Practices are defined by Bromiley and Rau (2014) as a specific act or set of actions that several firms may execute. Tang et al. (2022) utilized PBV as a theoretical lens to investigate the impact of CEPs on firms’ sustainable performance. Previous studies identified FA and CEPs as critical practices that can be adopted to improve firms’ performance. Similarly, this study focuses on PBV theory, which recommends that easy-to-simulate CEPs and fintech deployment will significantly impact banks’ NFP.

As stated by Tiwari et al. (2020) and Tang et al. (2022), PBV provides a considerable theoretical background for assessing performance consequences because it provides insights into the changes in firms’ performance resulting from the adoption of imitable actions. Regarding FA for banks, the PBV offers a theoretical foundation for innovative technologies, which facilitates successful technologies’ adoption, like fintech. In addition, the PBV provides a theoretical lens for CEPs that can be transmitted across organizations without the need for isolated mechanisms. Thus, this study is based on PBV theory to explore the impact of FA and CEP on banks’ NFP.

2.2. Financial Technology (Fintech)

Fintech is a critical topic and innovation that would promote research in many disciplines due to the importance of digital connectivity for firms’ sustainable performance. Fintech has received significant academic attention over the last few years due to its critical role in redefining conventional finance by enabling new technological innovations (Vergara and Agudo 2021) and the creation and delivery of 24 h/7 day financial services that meet customers’ expectations (Baker et al. 2023). Pizzi et al. (2021) referred to fintech as an example of industry 4.0 (I4.0) technologies that support firms’ transition into a sustainable business model. Fintech is defined by Leong (2018) as any innovative solutions that enhance financial services processes by suggesting technology solutions according to different business situations. Also, Vergara and Agudo (2021) and Thakor (2020) asserted that fintech services utilize innovation and advanced technologies to improve, expand, and simplify financial services to support businesses and stakeholders in conducting their financial operations.

The expansion of the fintech industry is related to some key drivers. First, customers were confronted with challenges in attaining traditional financial services during the financial crisis in 2008 (Baker et al. 2023). Second, the spread of the COVID-19 pandemic in 2020 forced companies to change their way of communicating with their customers. As a result, customers learned rapidly to use online banking services during the lockdown period (Chen et al. 2021). Third, the rapid growth of technological advancements is a critical reason for the emergence of fintech firms (El-Said 2023). Fourth, lower regulation for financial services provided by non-bank institutions is a major factor in fintech firms’ growth (Thakor 2020).

According to Mhlanga (2023), there are four stages of the fintech revolution. Fintech 1.0 was from 1866 to 1967. This period focused on the development of the infrastructure of economic globalization (Basdekis et al. 2022). The financial sector was related to technology but was an analog sector (Chen et al. 2021). It began with installing the first transatlantic cable and using commercial telegraphs (Kharrat et al. 2023). Fintech 2.0 was from 1967 to 2008, which is the time of digital financial services. During this period, transactions and communication were conducted digitally. Also, electronic payment and automated teller machines (ATMs) were introduced to the market as examples of fintech products. Also, online banking was introduced in America in 1980 (Chen et al. 2021). Fintech 3.0 was from 2008 to 2018 and arose in both developing and developed economies. Several internet financial firms have been developed, and traditional banks have started to offer digital products. Fintech 4.0 covers the period from 2018 until now. During this period, new disruptive technologies have emerged, such as AI and blockchain technologies, and they are considered key enablers of innovation that will change the future of financial services (Basdekis et al. 2022).

2.3. Fintech Adoption and Bank Performance

The nexus between the impact of FA and banks’ performance has been intensively examined in the literature. Chopra et al. (2013) mentioned that the impact of FA on the banking and financial industries is high due to the implementation of new technological innovations, advanced technological infrastructure, and new technological tools across the banking industry, which change the way it operates to fulfill customers’ expectations. Feng and Goli (2023) and Owusu (2017) stated that banks’ financial performance provides essential insights for stakeholders to assess the performance and can be measured by utilizing financial measures like profitability, liquidity, and valuation ratios (for example, ROA and ROE). Paulet and Mavoori (2019) referred to the significant importance of banking operations’ digitalization, like enhancing customer interactions, making decisions, and developing new business models. Also, Mitra and Karathanasopoulos (2020) examined the effect of fintech development on banks’ performance and found that traditional banks’ profitability is higher when they collaborate with fintech companies or when banks adopt their own financial technology in their business model. Bouheni et al. (2023) asserted that collaboration between banks and fintech firms is important to create new services for the bank’s customers. Several studies (Baker et al. 2023; Vergara and Agudo 2021; Chen et al. 2019) recommended banks’ FA to avoid the negative impacts of fintech firms’ growth and cope with the changes that the COVID-19 pandemic has brought about through offering new innovative solutions.

Through FA, banks can achieve more revenues by offering new financial products and services (Omarini 2018); focusing on user-friendly interfaces which impact users’ experience and loyalty (Chen et al. 2019); enhancing risk management techniques that promote accessibility to new data sources and use analytics tools (Leo et al. 2019); and improving efficiency, reducing operating expenses, and raising profitability (Lee and Shin 2018).

Due to the importance of performance for all firms, there are two paths to measure a firm’s performance: one is financial performance, and the other is non-financial performance (Chen et al. 2021). The existing literature focuses on banks’ financial performance. Kharrat et al. (2023) investigated the impact of FA on banks’ financial performance and explored the role of regulation as a mediator. Lee et al. (2021) and Zhao et al. (2022) conducted an empirical study of Chinese banks to test whether FA impacts banks’ efficiency of asset quality management and profitability. However, there is a dearth of studies in the literature exploring the impact of FA on banks’ non-financial performance, and empirical evidence is still inconclusive.

Chen et al. (2021) stated that NFP is related to corporate social responsibility, customer satisfaction, service quality, and work efficiency. Self-service technologies can enhance service quality and increase customer satisfaction, market share, and customer base among banks. Also, the successful adoption of fintech products in a bank enables employees to serve customers in an easy way while working remotely during the COVID-19 era. Thus, this study focuses on exploring the impact of FA on three perspectives of banks’ non-financial performance: customer satisfaction (CS), internal processes (IP), and learning and growth (LG) perspectives, which will be discussed in the following sections.

2.3.1. Fintech Adoption and Customer Satisfaction Perspective

Fintech is considered the most promising industry, particularly during the COVID-19 era, because the pandemic proves that digital banking is not an option but a mandatory requirement. Due to the COVID-19 implications, customers’ expectations have rapidly changed as they prefer greater convenience, reliability, trust, and easy-to-use financial services. In fact, fintech products have a significant effect on people’s daily tasks like real-time payment and loan approval (Akpan et al. 2020).

The customer satisfaction perspective refers to how customers see our banks. Customers’ satisfaction or dissatisfaction with banks’ financial products is based on a continuous measuring and monitoring process for assessing banks’ services, which supports banks adjusting their products and services to raise customers’ satisfaction and retention (Owusu 2017; Alkhazaleh and Haddad 2021). In addition, Dwivedi et al. (2021) stated that FA is influenced by perceptions about whether it is easy or difficult to use. As stated by Panchal and Krishnamoorthy (2019), Momaya (2019), and Ahn and Kim (2019), banks should first recognize customers’ perceptions before FA, because if customers are not able to access digital banking services through digital platforms, they are required to visit the bank’s branches. Furthermore, FA supports banks to provide many benefits for their customers, like saving their time and payment costs of visiting the bank’s branch, providing high-quality financial products and services (Dwivedi et al. 2021), and reducing the costs and efforts of accomplishing financial transactions (Al-Nawayseh 2020), which in turn increases customers’ satisfaction and retention. Ryu (2018) claimed that FA facilitates mobility and flexible access because customers can access it through smartphones. Chen et al. (2021) asserted that banks’ FA enables their customers to access more convenient and trustful financial services at any time. As stated by Yan et al. (2023) and Al-Nawayseh (2020), customers’ trust is a critical factor in adopting fintech products due to the confidential data involved in offering services. Trust in fintech applications refers to the customers’ confidence, integrity, and transparency in these applications.

In addition, FA enables banks’ clients to execute financial transactions with a self-service function, which results in increasing their participation and experience (Kaushik et al. 2019). Moreover, FA helps banks attract new customers who do not have bank accounts because there is no network signal at their remote location or because no bank branches exist at these locations (Chen et al. 2021). Yan et al. (2023) stated that FA enabled customers to complete financial transactions for free, like opening new accounts, which can create cost savings for customers and raise their preferences to adopt fintech products. Hence, we postulate the following:

H1. 

Fintech adoption is positively associated with banks’ customer satisfaction perspective.

2.3.2. Fintech Adoption and Internal Banking Processes Perspective

Recently, fintech development has had a considerable influence on the financial sector, particularly the banking sector. According to FSB (2017), FA can expand banking institutions in terms of scale and scope that promote the construction of intelligent banks, which enhance banks’ efficiency. As mentioned by Owusu (2017), the internal banking processes perspective refers to what the bank must excel at. Also, it was stated that the value proposition can be created and provided for customers through internal banking processes. FA can shape banking operations and activities because it can shape the way banks conduct business and helps banks adopt new technological applications that reduce banks’ costs overtime, like transaction costs, processing costs, administrative costs, and overall operational costs. Dwivedi et al. (2021) and Kharrat et al. (2023) asserted that FA has completely transferred banks’ traditional business model by streamlining internal processes, monitoring patterns, reducing operating costs, increasing efficiency and effectiveness, transferring financial services, and improving banks’ activities.

With the help of FA, banks can provide partially or totally automated machine services with less focus on labor, which results in reducing operational costs, like paperless services, optimizing resource utilization, increasing efficiency and effectiveness, and reducing work errors (Chen et al. 2021).

In addition, banking operations become more robust and efficient with the removal of intermediaries in providing financial services due to the adoption of new technological applications like artificial intelligence, machine learning, and blockchain (Milian et al. 2019), which results in reducing time to complete the work, work errors, and routine or inappropriate repetitive tasks (Owusu 2017). Therefore, we can hypothesize the following:

H2. 

Fintech adoption is positively associated with banks’ internal processes perspective.

2.3.3. Fintech Adoption and Learning and Growth Perspective

With the help of FA, banks can continuously reinvent their financial products and services in the digital world. As mentioned by Owusu (2017), the learning and growth perspective refers to how the banks can improve and create value continuously. Through FA, banks can offer new financial products and create value for customers. Dwivedi et al. (2021) stated that banks’ FA should be considered a strategic perspective aligned with strategic objectives and processes. So, banks’ employees are involved in implementing financial innovation strategies and focus on strategic issues rather than poring over paperwork (Wang et al. 2020), which enhances the bank’s performance. Also, FA helps banks automate their internal operations, which reduces human intervention and staff duplication, saves labor costs (Li and Xu 2021), reduces human judgments and errors in work, enhances service quality (Singh et al. 2021), and increases staff work efficiency (Bouheni et al. 2023). According to Chen et al. (2021), banks have enormous customer bases, and each branch does not have enough staff or time to handle clients’ complaints daily. So, FA supports banks to raise their staff productivity by reducing the service time per client, and more clients can be served daily. With the increasing adoption of fintech, banks provide training and learning programs for their staff to acquire sufficient professional knowledge and technical skills (Zhao et al. 2019); develop their complaint management skills; provide feedback services; and create an organizational change agenda (Tien and My 2022). Zhao et al. (2019) asserted that establishing an innovation-based culture and improving product innovation are critical factors for the success of banking service innovation. Thus, we can postulate the following:

H3. 

Fintech adoption is positively associated with banks’ learning and growth perspective.

2.3.4. Fintech Adoption and Circular Economy Practices

The circular economy (CE) is considered an economic system focused on business models (Gonçalves et al. 2022) that replace the linear economy through some alternatives such as reducing, reusing, recycling, or remanufacturing during production, distribution, and consumption processes (Kirchherr et al. 2017). Ghisellini et al. (2016) defined the CE as an economic system that promotes efficiency through waste reduction and continual resource usage. Ali et al. (2022) referred to the CE as a strategic option for firms which necessitates the restoration, renewal, and disruption of the economic system, posing significant challenges to the evolutionary processes of business models (De Sousa Jabbour et al. 2019). Previous studies emphasized the association between CE and digitalization to support firms that involve sustainable practices within their business models (De Sousa Jabbour et al. 2019; Tseng et al. 2018; Rosa et al. 2019). The CE is the fourth industrial revolution platform for adopting new business models (Lukic et al. 2023) and engaging all stakeholders to adopt circular models (Gupta et al. 2019). Financial technologies are significant enablers of the CE because technologies related to I4.0, like IOT, AI, and BD, can raise productivity and resource efficiency (Kristoffersen et al. 2020). Pizzi et al. (2021) referred to fintech as an example of a sector that emerged because of the I4.0 revolution, which promotes the CE’s adoption through providing new forms of financial resource accessibility. Based on PBV theory, it can be concluded that fintech provides a significant contribution towards banks’ transition into CE models through the utilization of several digital financial technologies, such as mobile payment platforms, AL, blockchain, and IOT. Fintech solutions offer both sell-side and buy-side payment solutions to facilitate the adoption of CEPs (Siddik et al. 2023a). Additionally, information disclosures, insurance services, and risk assessment can be promoted by fintech solutions (Siddik et al. 2023a), consequently accelerating banks’ transition into CE models. Thus, based on these arguments, the hypothesis is developed as follows:

H4. 

Fintech adoption is positively related to banks’ CEPs.

2.3.5. Circular Economy Practices and Banks’ Customers Satisfaction Perspective

Lukic et al. (2023) stated that banks’ engagement in the CE contributes to socially responsible behavior, greater profitability, and achieving competitiveness in the digital era. Drawing from PBV theory, banks are interested in adopting and financing CEPs to change their customers’ perceptions, particularly after the financial crisis of 2008. So, banks’ transition into CE models enables stakeholders to trust the banks. Feng and Goli (2023) asserted that banks’ adoption of CEPs can enhance their reputation, attracting socially responsible stakeholders. In addition, Ozili (2021) stated that banks can provide additional loans to customers who need to transmit into the CE, which results in attracting new circular customers and raising their satisfaction and loyalty. Feng and Goli (2023) stated that the CE transition enables banks to innovate new products and processes and embrace new innovative business models such as product-as-a-service (PAAS) or remanufacturing, which enhance service quality and raise banks’ profitability and customer satisfaction. Hence, this study hypothesizes the following:

H5. 

CEP adoption is positively related to banks’ customer satisfaction perspective.

2.3.6. Circular Economy Practices and Banks’ Internal Processes Perspective

De La Cuesta-González and Morales-García (2022) stated that banks are required to engage in the CE during the digital era. Not only do banks’ transitions into the CE result in environmental benefits, but it also increases banks’ productivity and flexibility, reducing work errors and operational costs. Lukic et al. (2023) mentioned that banks’ internal processes are focused on recording all transactions electronically rather than utilizing paper forms and documents; consequently, green banks can be created. Based on PBV theory, the adoption of CEPs can ameliorate banks’ IPs. Involving CEPs in internal bank operations results in optimizing resource utilization (Geissdoerfer et al. 2017), waste reduction (Ali et al. 2022), and enhancing productivity, resource efficiency, and service quality (Kirchherr et al. 2017). Moreover, the CE is an essential strategic tool for banks to achieve a competitive advantage. Banks are required to provide sufficient funds to circular projects and make internal transitions towards CE models (Lukic et al. 2023) through adjusting existing financial models, providing new financial models for the CE, or providing new credit lines for circular businesses (Ali et al. 2022). Additionally, banks have involved in their lending policies environmental protection aspects and energy efficiency, which accelerate CE development (Lukic et al. 2023). Hence, the following hypothesis is developed:

H6. 

CEP adoption is positively related to banks’ internal processes perspective.

2.3.7. Circular Economy Practices and Banks’ Learning and Growth Perspective

Banks have a critical role in the growth of the CE. Ozili and Opene (2021) stated that bank management should internalize its commitment towards CE transition, ensuring that staff at all levels recognize the value of waste and strive to reduce waste as a work culture. Therefore, banks should set the CE as a strategic priority, be involved in their strategies and policies, and establish an appropriate infrastructure supporting CE adoption. Additionally, bank management should provide training programs for its staff to acquire sufficient knowledge and understanding about new features of the CE and CE laws and regulations, ensuring that banks have an effective monitoring system to monitor compliance with laws and regulations (Ali et al. 2022; Ozili and Opene 2021). Thus, this study hypothesizes the following:

H7. 

CEP adoption is positively related to banks’ learning and growth perspective.

2.3.8. Mediating Effects of Circular Economy Practices

I4.0 technologies support firms in adopting CEPs by providing real-time information about processes, operations, and resource movement, which facilitates tracking, monitoring, and making long-term decisions (Siddik et al. 2023a). On the other hand, there is intensive pressure on banks to provide sufficient funds to circular businesses and to enterprises that need to transmit into the CE model (Ozili 2021). Therefore, fintech is one of the I4.0 technologies that can support the adoption of CEPs through providing several funding channels, which, if successfully adopted, can enhance banks’ NFP. FA may indirectly impact banks’ NFP through restructuring their business models into circular ones. Hence, based on the preceding discussions, this study is the first to investigate the mediating role of CEPs between FA and banks’ NFP and suggest that CEPs mediate the relationship between FA and three perspectives of banks’ NFP. First, Siddik et al. (2023a) stated that FA facilitates transition into CE models through accessing several technologies like IOT, AI, and mobile payment, which in turn enables the banks to involve CEPs in their business operations. As a result, banks can innovate new financial products and business models, offer new credit lines for circular projects, and raise lending to circular clients (Ozili 2021). Consequently, it results in addressing new market segments, extending customer experience, gaining a large market share of clients in the CE (Pizzi et al. 2021; Bocken et al. 2014), attracting new circular customers, and enhancing existing customers’ satisfaction (Ozili and Opene 2021). Thus, this study hypothesizes the following:

H8. 

CEP adoption mediates the relationship between FA and banks’ customer satisfaction perspective.

Second, fintech promotes the adoption of the CE, which is a restoring and regenerative system that optimizes resource utilization, enhances efficiency and productivity, and increases the opportunities for innovation, job generation, and economic growth (Gonçalves et al. 2022). On the other hand, prior studies (Kirchherr et al. 2017; Ali et al. 2022; Suárez-Eiroa et al. 2019) stated that CEPs are critical enablers for improving banks’ productivity and efficiency by optimizing resource usage, waste reduction, and creating economic value for banks. Also, by adopting CEPs, banks are considered to have responsible bank principles, ensuring that banks are engaged in funding activities that promote a sustainable environment (Ozili 2021). Hence, the hypothesis is developed as follows:

H9. 

CEP adoption mediates the relationship between FA and banks’ internal processes perspective.

Third, digitalization is a key enabler of adopting CEPs in banks, subsequently enhancing their performance if managed effectively (Bressanelli et al. 2022). Banks are required to develop a common understanding of the CE to help them in determining, selecting, and financing activities focused on new CE models (Ozili and Opene 2021), promoting a strong culture of the CE at all of a bank’s levels, establishing an appropriate infrastructure for CE transition (De La Cuesta-González and Morales-García 2022), and developing their staff’s skills and capabilities to engage in CEPs (Ozili 2021). Also, the adoption of CEPs can promote job generation by providing new job opportunities by closing the loop in banking operations, which enhances banks’ staff productivity and satisfaction (Feng and Goli 2023). Thus, this study hypothesizes the following:

H10. 

CEP adoption mediates the relationship between FA and banks’ learning and growth perspective.

The proposed conceptual model of the study is shown in Figure 1 as follows.

3. Research Methodology

This section presents a presentation of the study’s research methods, population, sample techniques, and sources of data collection. The study has two main goals: first, to explore FA’s impact on banks’ NFP in developing countries, and second, to explore the mediating role of CEPs on the relationship between FA and banks’ NFP. To attain these goals, banking institutions in Iraq, Egypt, Jordan, and Oman as developing countries have been considered for some reasons: first, these Arab countries attempt to cope with the rapid growth of fintech because it is a fast-growing sector that achieves a significant investment return, supporting the achievement of countries’ sustainable development (Alkhazaleh and Haddad 2021); second, banking institutions in Egypt (Marwa 2022; Hassouba 2023), Oman (Rubaiai and Pria 2022; Khan et al. 2023), Jordan (Alkhazaleh and Haddad 2021; Almashhadani et al. 2023), and Iraq (Neama et al. 2023) have a significant contribution to FA and CEPs, which consequently improve their NFP. Therefore, the high bank adoption rate of fintech in these countries is a critical motivation for conducting this study.

3.1. Research Design, Sampling Techniques, and Data Collection

This study utilizes quantitative and descriptive research methods that explain empirical phenomena with statistical data, characteristics, and relationships’ patterns among variables (Al-Shari and Lokhande 2023; Lontchi et al. 2023). A structured questionnaire was developed by defining various measures derived from a literature review and was separated into two sub-sections. The first section involved respondents’ demographic information related to age, gender, academic qualification, job position, and years of experience in the banking industry. The second section focused on measurement items, which were graded on a 5-point Likert scale ranging from strongly disagree = 1 to strongly agree = 5 and covered all issues of FA, CEPs, and CS, IP, and LG perspectives of banks’ NFP. All measurement items in this study were altered according to the research objectives. All questionnaire items and relevant studies are shown in Appendix A.

The purpose of the questionnaire is to collect primary data from banks’ staff in Iraq, Egypt, Jordan, and Oman. To evaluate the reliability and validity of the survey instrument items, a pilot test was conducted among 15 different professionals who were selected from the academics and banking sectors and had sufficient knowledge in the subjects of FA, CEPs, and their relevance to the banking sector. Due to the large size of banks’ staff population, a sample size was determined based on a 10:1 ratio, as recommended by Hair et al. (2014) and Saptono and Khozen (2023). A minimum sample size of 380 respondents was needed for this study because, initially, 38 measurement items in the questionnaire should be considered. To avoid a lower response rate, 943 surveys were distributed through several social media platforms (WhatsApp, Facebook, LinkedIn, and Instagram) and e-mails. This method is efficient and maintains diversity and random responses (Ali et al. 2022). This study considers 15 banks operating in Iraq, Egypt, Jordan, and Oman; 397 respondents were chosen by using a convenience sampling technique; and they occupied various positions and had different experience levels as described in Appendix B. Data were collected during the period from 8 December 2023 to 15 February 2024. We initially collected a total of 550 questionnaires, and after preliminary screening, 153 of them were excluded due to invalid values. Thus, the actual sample size for this study was 397, with a response rate of 72.18%.

3.2. Survey Instrument Development

The research’s hypotheses were assessed by using several questionnaire items. The study’s measurement items were derived from a literature review to assess the constructs (FA, CEPs, CS, IPs, and LG) used in this study. This study collected primary data to assess the potential impact of FA on banks’ NFP with the mediating effect of CEPs. As shown in Appendix A, the measurement variables are discussed in the following sections.

3.2.1. Independent Variable

The independent variable in this study is FA. There is a dearth of the literature related to FA in the context of banking institutions in developing countries. Therefore, 8 items were derived from Baker et al. (2023) and Dwivedi et al. (2021) and adjusted according to the study’s context. The FA scale measures the bank staffs’ opinions about the effect of their banks’ FA motives on CEPs, CS, IPs, and LG. For example, FA helps your bank innovate new products and services.

3.2.2. Dependent Variable

The dependent variable of this study is banks’ NFP. To measure NFP, this study obtained data on three perspectives of banks’ NFP: customer satisfaction (CS), internal processes (IPs), and learning and growth (LG). Banks’ CS perspective was measured through nine items derived from (Lontchi et al. 2023; Chen et al. 2021; Al-Shari and Lokhande 2023). For example, the respondents were asked whether FA reduces the interaction between customers and bank staff. Banks’ IP perspective was measured through six items derived from (Dwivedi et al. 2021; Chen et al. 2021; Owusu 2017). For example, the respondents were asked whether FA raises the bank’s productivity and flexibility. Furthermore, banks’ LG perspective was measured through seven items derived from (Basdekis et al. 2022; Chen et al. 2021; Owusu 2017). Examples of these items include whether FA enhances employees’ productivity.

3.2.3. Mediating Variable

This study incorporates CEPs as a mediating variable to investigate whether CEPs mediate the association between FA and banks’ NFP. Eight items were derived from Ali et al. (2022), Pizzi et al. (2021), and Ozili (2021) to measure the CEP scale. The respondents were asked whether their bank management is serious about adopting a circular economy to attain the sustainable development goals (SDGs).

3.3. Data Analysis Techniques

Consistent with Hair et al. (2019), this study utilized the partial least squares structural equation modeling (PLS-SEM) technique (Smart PLS software version 3.3.3) to investigate the relationship between FA and banks’ NFP alongside the mediating effect of CEPs. It is relevant for PLS-SEM to be used in this study due to some reasons: first, to explore the role of the mediating variable, SEM is better than regression in executing estimations (Preacher and Hayes 2004); second, PLS-SEM considers measurement errors and presents a more accurate estimate of the mediation effect (Hair et al. 2019; Elmaasrawy et al. 2024); and third, PLS-SEM is relevant for complex and simple experimental research (Al-Shari and Lokhande 2023). Overall, a research methodology flowchart is presented in Figure 2.

4. Data Analysis and Results

Data analysis is presented in this section through the following three stages: first, an analysis of the respondents’ demographic information; second, a measurement model of the study; and third, the assessment of the structural model and hypotheses testing.

4.1. Demographic Information of Respondents

Demographic information about respondents is shown in

Table 1. Demographic profile of the respondents.

Items	Description	Frequency	Percentage
Gender	Male	245	61.71%
	Female	152	38.29%
	Total	397	100%
Age	34 or under	40	10%
	From 35 to 40	286	72%
	From 41 to 45	31	8%
	From 46 to 50	20	5%
	More than 50	20	5%
	Total	397	100%
Academic Qualification	Bachelor’s degree	270	68%
	Master	79	20%
	Ph.D	28	7%
	Others	20	5%
	Total	397	100%
Position	Branch Manger	30	7.5
	Assistant Manager	55	13.85
	Financial Analyst	45	11.42
	Accountant	200	50.37
	Loan officer	52	13.09
	Others	15	3.77
	Total	397	100%
Field of Study	Accounting	230	57.93%
	Banking and Finance	80	20.15%
	Management	40	10.07%
	Others	47	11.85%
	Total	397	100%
Experience	Less than 5 years	32	8%
	5 to less than 10 years	306	77%
	10 to 15 years	11	3%
	More than 15 years	48	12%
	Total	397	100%
Bank Classification	Public	7	1.76%
	Commercial	385	96.97%
	Islamic	5	1.27%
	Total	397	100%

. Most respondents were from commercial banks in Iraq, Egypt, Jordan, and Oman. The final sample size is 397, consisting of 245 (61.71%) males and 152 (38.29%) females. The age distribution statistics indicate that most respondents were between 35 and 40 years old (72%) and had worked in the banking sector for more than 5 years (77%), resulting in a higher stability of the questionnaire. Regarding academic qualifications, most respondents held bachelor’s degrees (68%) while a lower percentage held master’s and Ph.D. degrees (27%) and were employed as branch and assistant managers, 7.5% and 13.85%, respectively, accountants (50.37%), financial analysts (11.42%), loan officers (13.09%), and others (3.77%). Additionally, most respondents were accounting majors (57.93%), followed by banking and finance (20.15%), management (10.07%), and others (11.85%).

4.2. Assessment of Measurement Model

Tests of reliability and validity were executed to assess the measurement model. The constructs’ reliability and validity are presented in

Table 2. Results of model validity and reliability test.

Construct	Items	Factor Loadings	VIF	Cronbach Alpha (CA)	Composite Reliability (CR)	Average Variance Extracted (AVE)
		Must Be > 0.7	Must Be < 5	Must Be > 0.7	Must Be > 0.7	Must Be > 0.5
Fintech Adoption (FA)	FA1	0.740	1.973	0.888	0.898	0.599
	FA2	0.756	2.541
	FA3	0.776	2.617
	FA4	0.841	2.812
	FA5	0.744	2.041
	FA6	0.701	2.256
	FA7	0.847	2.875
Circular Economy Practices (CEPs)	CEP1	0.846	2.480	0.926	0.929	0.772
	CEP2	0.858	2.621
	CEP3	0.911	4.822
	CEP4	0.917	4.380
	CEP5	0.860	3.313
Internal Process Perspective (IP)	IP1	0.845	2.489	0.890	0.896	0.695
	IP2	0.780	1.910
	IP3	0.814	2.582
	IP4	0.855	2.737
	IP5	0.817	2.525
Customer Satisfaction Perspective (CS)	CS1	0.898	4.211	0.867	0.870	0.657
	CS2	0.712	1.516
	CS3	0.772	1.898
	CS4	0.812	2.099
	CS5	0.847	3.717
Learning and Growth Perspective (LG)	LG1	0.803	1.922	0.865	0.869	0.650
	LG2	0.773	1.828
	LG3	0.864	2.893
	LG4	0.823	2.559
	LG5	0.764	1.740

. Factor loading is utilized to test the reliability of individual items. According to Hair et al. (2014), an item’s reliability is accepted when its factor loading is greater than 0.7. As presented in Table 2, all factor loadings exceed 0.7, which confirms the reliability of the items utilized in this study except FA8 of the FA construct; CS6, CS7, CS8, and CS9 of the CS construct; IP6 of the IP construct; LG6 and LG7 of the LG construct; and CEP6, CEP7, and CEP8 of the CEP construct. These weak items were excluded from further investigation due to their factor loadings, which are less than 0.7. Additionally, Cronbach’s alpha (CA) and composite reliability (CR) values were calculated to validate the internal consistency of the constructs. According to Hair et al. (2014), CA and CR values must be greater than 0.7 to be acceptable. The findings in Table 2 revealed that CA and CR values for all items exceed 0.7. Thus, the study’s model fulfills internal consistency standards. Moreover, convergent validity (CV) reveals the degree of correlations among many items for a variable, which is assessed by average variance extracted (AVE) and factor loadings (Al-Shari and Lokhande 2023; Hu et al. 2019). AVE values refer to the percentage of variation explained by a construct’s items, which should exceed 0.5 as recommended by Fornell and Larcker (1981). All AVE values in Table 2 ranged from (0.599) to (0.772), which is greater than the acceptable value of 0.5. Thus, the study’s variables confirm CV.

Additionally, the Fornell–Larcker criterion is utilized to assess the discriminant validity (DV) of the research constructs. According to Fornell and Larcker (1981), the AVE square root value of each construct should exceed its correlation with other constructs. As indicated in

Table 3. Discriminant validity test: Fornell–Larcker criterion.

Variables	FA	CS	IP	LG	CEP
FA	0.822
CS	0.772	0.841
IP	0.769	0.739	0.814
LG	0.748	0.701	0.777	0.785
CEP	0.711	0.712	0.705	0.743	0.779

, the findings indicate that the correlations between each set of constructs do not exceed the square root of their AVE.

Furthermore, the multicollinearity problem was assessed in this study before investigating the structural model via the variance inflation factor (VIF). Hair et al. (2019) recommended that the VIF value must be less than 5 to verify the absence of multicollinearity problems among constructs. As presented in Table 2, the VIF values for all constructs are less than 5, which confirms the nonexistence of multicollinearity. Consequently, the model is suitable for PLS-SEM.

4.3. Structural Equation Model and Hypotheses Testing

The SEM method is suitable for this study due to some reasons: first, it does not require a sufficient theoretical basis to support validation and explanatory research and it is relevant for predictive and exploratory research with a smaller sample size whilst achieving high levels of statistical predictive ability (Hair et al. 2014; Tawfik and Elmaasrawy 2022; Elmaasrawy and Tawfik 2024); second, it is suitable for predicting complex models and developing theories; third, it imposes few restrictions on sample size and non-normal distributions (Leong et al. 2020); and finally, it can deal with the relationship between several variables, explain the causal relationships between independent variables and dependent variables, and it is used in many different fields (Hinson and Utke 2023).

According to Hayes (2009), some conditions must be fulfilled simultaneously to confirm the acceptance of the research hypotheses: first, the direct impact should have a significant effect; second, the path coefficient for the direct effect should be within the confidence interval; and third, the confidence interval does not involve zero. After the reliability and validity analysis, SEM is utilized in this section to test the research hypotheses, and the findings are shown in Figure 3.

Also, direct and indirect paths among FA, CEPs, CS, IPs, and LG are analyzed, and

Table 4. Results of hypotheses testing.

Hypothesis	Relationship	Path Coefficient β	T Statistics (|O/STDEV|)	p-Value	Confidence Intervals		Support for Hypothesis
					Minimum	Maximum
H1	FA -> CS	0.537	5.097	0.000	0.336	0.749	Yes
H2	FA -> IP	0.540	4.201	0.000	0.305	0.811	Yes
H3	FA -> LG	0.595	5.045	0.000	0.337	0.806	Yes
H4	FA -> CEP	0.711	12.593	0.000	0.603	0.824	Yes
H5	CEP -> CS	0.330	3.203	0.001	0.138	0.542	Yes
H6	CEP -> IP	0.321	2.247	0.025	0.031	0.593	Yes
H7	CEP -> LG	0.320	2.593	0.010	0.101	0.560	Yes
H8	FA -> CEP -> CS	0.235	3.095	0.002	0.102	0.404	Yes
H9	FA -> CEP -> IP	0.228	2.313	0.021	0.024	0.420	Yes
H10	FA -> CEP -> LG	0.228	2.540	0.011	0.074	0.428	Yes

presents the path coefficient, p-value, and t-value of each hypothesis. If the t-value exceeds 1.96 or the p-value is less than 0.05, the proposed hypotheses are supported, and vice versa.

The findings indicated that hypotheses 1, 2, 3, and 4 are confirmed, which means that FA positively and significantly impacts CS (β = 0.537, t-value = 5.097, p-value = 0.000), IPs (β = 0.54, t-value = 4.201, p-value = 0.000), LG (β = 0.595, t-value = 5.045, p-value = 0.000), and CEPs (β = 0.711, t-value = 12.593, p-value = 0.000), respectively. Moreover, the direct impacts of CEPs on CS, IPs, and LG were confirmed (β = 0.330, t-value = 3.203, p-value = 0.001; β = 0.321, t-value = 2.247, p-value = 0.025; and β = 0.320, t-value = 2.593, p-value = 0.010, respectively). Thereby, hypotheses 5, 6, and 7 are accepted.

Finally, the results of the mediation analysis indicate that CEPs have positive and significant mediating impacts on FA-CS, FA-IP, and FA-LG linkages (β = 0.235, t-value = 3.095, p-value = 0.002; β = 0.228, t-value = 2.313, p-value = 0.021; and (β = 0.228, t-value = 2.540, p-value = 0.011, respectively). Therefore, hypotheses 8, 9, and 10 are asserted. Moreover, R-Square (R²) is another critical tool that was utilized to assess the relationships of structural models and measure the predictive relevance of the models (Henseler et al. 2016). As shown in

Table 5. Coefficient of determination (R²).

Constructs	R-Square (R2)	Adjusted R-Square
CS	0.650	0.638
IP	0.641	0.629
LG	0.727	0.717
CEP	0.505	0.497

, all R² values exceed 0, which reveals that the capability for prediction is verified.

Also, it is common to calculate the effect size (ƒ²) of each path coefficient in the structural model to indicate whether an independent variable influences a dependent variable. When ƒ² values are greater than 0.3, 0.15, and 0.02, the effect size will be large, moderate, and small, respectively (Hayes 2009; Tawfik and Elmaasrawy 2024). The findings are summarized in

Table 6. Effect size (ƒ²) of each path coefficient.

Constructs	ƒ2	Effect Size
FA -> CS	0.408	Large
FA -> IP	0.402	Large
FA -> LG	0.641	Large
CEP -> CS	0.164	Small
CEP -> IP	0.152	Small
CEP -> LG	0.186	Small

as follows.

5. Discussion and Results

Although the usage of fintech applications has been raised in developed countries, its adoption is still in the early stages in developing countries. However, FA is growing rapidly in developing countries, particularly during the COVID-19 era. Prior studies (Baker et al. 2023; Liu et al. 2021; Omarini 2018) have explored FA in the financial industry, especially the banking sector, and focused only on exploring the association between FA and banks’ financial performance. Additionally, most studies investigated FA, CEPs, and banks’ performance independently; however, the interplay among these variables is still inconclusive. Therefore, this study responds to the call of many scholars (Tien and My 2022; Zhao et al. 2022; Dasilas and Karanović 2023) to investigate the impact of FA on banks’ NFP in developing countries, considering CEPs as a mediator. Consequently, this is the first study that incorporates FA, CEPs, CS, IPs, and LG variables under a unified conceptual model. This study confirmed that FA enables banking institutions to lay the groundwork for offering new products and services that support banks to be in a good position and achieve a sustainable competitive advantage. To achieve research objectives and answer research questions, this study utilized SEM to test the research hypotheses.

Several contributions are provided to the literature on the association among FA, CEPs, CS, IPs, and LG in the setting of banking institutions in developing countries. First, the study’s findings confirmed the hypothesized effect of FA on the CS, IP, and LG perspectives, implying that FA positively and significantly impacts banks’ NFP, as indicated in Figure 3 and Table 4. Consistent with (Ryu 2018; Chen et al. 2021; Yan et al. 2023; Al-Nawayseh 2020), the findings indicated that FA positively and significantly impacts banks’ CS (β = 0.537, p-value = 0.000, ƒ² = 0.408 large, and confidence interval “0.336, 0.749” does not include 0) through offering new innovative and convenient products and services at lower costs and facilitating mobility and flexible access, which in turn increases customers’ satisfaction and retention. Thus, fintech has become part of users’ daily lives and has become an essential part of their daily activities; thereby, H1 is accepted. Moreover, the findings asserted the positive and significant impacts of FA on banks’ IPs (β = 0.54, p-value = 0.000, ƒ² = 0.402 large, and the confidence interval “0.305, 0.811” does not include 0), which aligns with the findings of Dwivedi et al. (2021), Kharrat et al. (2023), and Owusu (2017), who stated that banks’ adoption of I4.0 technologies like fintech could lead to streamlining internal processes, optimizing resource utilization, increasing efficiency, and reducing work errors. Thus, H2 is supported. Additionally, it is found that FA has positive and significant impacts on banks’ LG perspective (β = 0.595, p-value = 0.000, ƒ² = 0.641 large, and the confidence interval “0.337, 0.806” does not include 0); thereby, H3 is confirmed. This finding is analogous to the observations of Li and Xu (2021), Singh et al. (2021), and Bouheni et al. (2023), who referred to the critical role of FA in automating banks’ internal operations, establishing a technologically supported culture, and providing training and learning programs for their staff.

Second, the study’s findings confirmed the positive and significant relationship between FA and CEPs (β = 0.711, p-value = 0.000, and the confidence interval “0.603, 0.824” does not include 0); thereby, H4 is confirmed. This result validates the findings of Kristoffersen et al. (2020), Pizzi et al. (2021), and Siddik et al. (2023a), reflecting that FA facilitates banks’ transition towards CE models through providing new forms of financial resource accessibility, utilizing digital financial technologies, and simplifying information disclosures, insurance services, and risk assessment.

Third, the findings also revealed a positive and strong direct relationship between CEPs and the CS (β = 0.330, p-value = 0.001, ƒ² = 0.164 small and confidence interval “0.138, 0.542” does not include 0), IP (β = 0.321, p-value = 0.025, ƒ² = 0.152 small and confidence interval “0.031, 0.593” does not include 0), and LG (β = 0.320, p-value = 0.010, ƒ² = 0.186 small and confidence interval “0.101, 0.560” does not include 0) perspectives of banks’ NFP, respectively; thereby, hypotheses 5, 6, and 7 are supported. Although this is the first attempt to explore the linkage between CEPs and the NFP of banking institutions in developing countries, few studies argued that banks’ transition into CE models enhances their reputation and raises stakeholders’ trust with them, which leads to attracting new circular customers and raising their satisfaction and loyalty (Lukic et al. 2023; Feng and Goli 2023), optimizing resource utilization (Geissdoerfer et al. 2017), waste reduction (Ali et al. 2022), enhancing productivity, and service quality (Kirchherr et al. 2017). This result also confirms PBV theory, which demonstrates that imitable practices like CEPs can enhance banks’ NFP.

Finally, the mediation analysis proved that CEPs positively and significantly mediate the association between FA and the CS, IP, and LG perspectives of banks’ NFP, thus, H8, H9, and H10 are accepted. This result implies that FA is a critical enabler of adopting CEPs, which, if successfully implemented, would improve banks’ NFP. So, this finding indicates that FA has direct and indirect impacts on banks’ NFP through the adoption of CEPs. As no previous study has explored the linkage between FA and banks’ NFP alongside the mediating role of CEPs in developing countries like Jordan, Oman, Egypt, and Iraq, there is no supportive literature; thus, this study contributes to the existing literature.

6. Conclusions, Implications, Limitations, and Suggestions for Future Research

Fintech is considered a paradigm shift in the financial sector. Although technology has been a part of the financial service sector since the 1850s, fintech has only received significant attention during the past two decades to demonstrate technological advancements which can transfer the provision of financial services and innovate new business models, processes, and products. Consistent with Al-Nawayseh (2020), fintech is one example of I4.0 technologies which replace traditional financial services and make financial transactions less expensive, secure, and more convenient. El-Said (2023) and Chen et al. (2019) asserted that fintech firms use advanced technologies that can modify traditional banking operations. On the other hand, Dasilas and Karanović (2023) stated that banks should deploy sufficient funds to modernize their operations and provide new services to customers to face severe competition from fintech firms.

Based on the relevant literature reviewed, the impact of fintech is mainly considered in developed countries, even though the fintech revolution has also occurred in developing countries. However, there is a dearth of studies that highlight the influence of FA on banking institutions in developing countries, particularly on their NFP. Accordingly, this study aims at investigating the impact of FA on banks’ NFP in developing countries and exploring how CEPs mediate the relationship between FA and banks’ NFP. Thus, the current study is the first scholarly paper that considers the interplay among these constructs. To achieve research objectives and answer research questions, SEM is employed in this study to test the research hypotheses in the proposed conceptual model. The study has successfully asserted the effect of FA on the CS, IP, and LG perspectives of banks’ NFP in developing countries such as Iraq, Egypt, Jordan, and Oman. Additionally, the study confirmed the importance of CEPs as a mediator in the linkage between FA and banks’ NFP. The empirical findings indicated positive and significant impacts of FA on CEPs and the CS, IP, and LG perspectives of banks’ NFP, which answered RQ1. Moreover, the findings revealed that CEPs have a positive and significant mediating role in the link between FA and the CE, IP, and LG perspectives, which answered RQ2. Finally, the current study provides significant theoretical and practical implications and can be discussed in the following sections.

6.1. Theoretical Implications

The study’s findings provide several noteworthy theoretical implications for the existing literature, as listed below.

First, this study contributes to the literature on the banking sector in developing countries by endorsing the principles of PBV theory to explore FA’s impacts on banks’ NFP. To the best of our knowledge, this is the first study which asserts the linkage between FA and the three perspectives of banks’ NFP. Moreover, the mediating role of CEPs is a critical contribution to this study. Drawing from PBV theory, the study proves that FA has a positive and significant impact on CEP adoption and the CS, IP, and LG perspectives of banks’ NFP.

Second, this study contributes to the existing literature through investigating the direct relationship between CEPs and the three perspectives of banks’ NFP, which is another critical theoretical contribution of this study. The study’s findings indicated that there is a positive and significant relationship between CEPs and the CS, IP, and LG perspectives of banks’ NFP, which is compatible with PBV theory.

Third, the current study extends the CE literature by investigating the mediating role of CEPs on the association between FA and banks’ NFP. The existing literature demonstrated CEPs as an example of I4.0 technologies ignoring their mediating role. Therefore, to the best of our knowledge, this is the first study to explore the mediation role of CEPs on the relationship between FA and banks’ NFP. Based on PBV theory, the study provides empirical evidence that CEPs positively and significantly mediate the association between FA and CEPs and the CS, IP, and LG perspectives of banks’ NFP.

Fourth, this is the first study that incorporates the FA, CEP, CS, IP, and LG variables under a unified conceptual model in the context of banking institutions in developing countries.

6.2. Practical Implications

The study’s findings provide significant implications and suggestions for banks’ managers, regulators, and policymakers that can be summarized as follows.

6.2.1. Banks’ Managers Perspective

First, the findings of testing the proposed conceptual model revealed that FA and CEPs have positive and significant impacts on the CS, IP, and LG perspectives of banks’ NFP. Thus, the incorporation of FA and CEPs into their daily routine operations is a critical issue for enhancing their NFP.

Second, the study’s findings present several recommendations for banks’ managers to adopt advanced technological tools, establish an innovative CE culture within their banks, invest in environmentally friendly programs to enhance their performance, and assess the extent to which banks’ activities contribute to each sustainable development goal (SDG).

Third, in the light of the study’s findings, banks’ managers should recruit professional experts with sufficient technological background to enhance the technical level of the banking system.

6.2.2. Policymakers and Regulators Perspective

First, the study’s findings indicate that CEPs have positive and significant effects on the CS, IP, and LG perspectives and positively and significantly mediate the association between FA and banks’ NFP. Thereby, policymakers and regulators should establish a legislative framework for the CE that guides banks to invest in CE models.

Second, regulators should provide waste infrastructure and recycling technology to enable banks to finance circular businesses without fear of low profit margins.

Third, policymakers and regulators in developing countries can promote the adoption of CEPs through providing reward systems for banks that adopt the latest I4.0 technologies like fintech, AI, IOT, and blockchain, which consequently contribute to the SDGs.

Fourth, regulators and lawmakers should prioritize fintech localization and sustainable goals, following the abilities and objectives of specific countries. As each country’s capabilities to adopt fintech and CEPs vary, the government is required to comprehend these concepts and involve them in national and economic goals.

6.3. Social Implications

The study’s results have valuable social implications, as listed below.

First, according to the study’s findings, there are evident requirements to fulfill the adaptation priorities of developing countries by providing fintech-related courses and training programs with professional trainers who have sufficient knowledge to enable first-time users to understand fintech’s concept and how to use its services at a younger age. Second, it is recommended to provide training programs about the CE in communities and public places to promote a culture of CE transition.

6.4. Limitations and Suggestions for Future Research

The study is considered the first one in developing countries to empirically test the impact of FA on banks’ NFP while considering the mediating role of CEPs. Although this study provides valuable contributions, it has some limitations to be addressed in future research, as listed below.

First, the study’s findings are limited to the banking sector in some developing countries, such as Iraq, Egypt, Jordan, and Oman, which restricts the findings’ generalization in terms of cultural differences. Thus, future research should be conducted in other countries and sectors to verify the theory and validate the findings’ applicability and validity.

Second, this study emphasizes FA’s impact on banks’ NFP (service industry). So, future research needs to be conducted in the manufacturing sector.

Third, the current study explored the impact of FA on banks’ NFP, considering CEPs as a mediator. Thus, future research is required to investigate other moderators such as green finance, green innovation, or financial literacy to improve the explanatory power of the current research model.

Fourth, the current study focuses on primary data (a questionnaire). Thus, future studies can be conducted to utilize the study’s variables and secondary data and identify the differences between them.

Fifth, this study employs PLS-SEM for testing research hypotheses. Therefore, PLS-SEM and contemporary methodologies such as artificial neural networks (ANNs) can be used in further research to validate the findings.