Abstract
This research suggests a study model of determinants of investment decisions in the context of financial innovation. The paper considers the technology innovation dimension, which influences stakeholders' (customer and employee) satisfaction, bank performance, and investment decisions. This research aims to bridge the gap by analyzing the effect of the innovation component, financial innovation on investment decisions and considering the mediated link via stakeholders' satisfaction and the bank's performance as well as the moderating role of Internet security (IS) and utilizing data gathered from 575 banks' employees and customers in Congo. Employing Structural Equation Modelling to evaluate the hypotheses, the research obtains that: (1) financial innovation (FI) is negatively associated with investment decisions (ID), and positively related to employee satisfaction (ES), customer satisfaction (CS), and bank performance (BP). (2) BP, CS, ES, and IS are related significantly and positively to ID. (3) BP, CS, and employee satisfaction do mediate the relationship between FI and ID. (4) Internet security does not moderate the effect of FI on ID. The investigation emphasizes the role of financial innovation and Internet security in investment decision-making for the providers of financial innovation services. Certainly, it recommends that financial institutions should consider this as well as internet security in their investment decisions. This article contributes to financial literature and bank management by offering supporting theories and a framework that facilitates the identification of pertinent strategic resources and constructs.
Keywords: Bank performance, Financial innovation, Investment decisions, Stakeholders satisfaction, Structural equation modelling
1. Introduction
The internet has become vital in practically every field. One of the most important industries in terms of Internet presence and services is banking [1]. Bank customers can profit from e-commerce by using the internet as an enabler technology [2]. Through financial innovation, most banks have updated business goals, and procedures to maximize benefits and reduce costs [2,3]. As a result, these improved plans, policies, and strategies allow clients to access their numerous bank accounts and handle transactions from anywhere [4]. The key concerns of both the banking sector and clients are the security and privacy of online transactions and personal data [5]. Internet security concerns include malware, adware, spyware, phishing, key-loggers, viruses, and Trojans. These threats and risks can manipulate bank clients and their personal data for illegal profit [5].
The term “capital” is used in investment decisions to refer to tangible assets such as plants, construction, raw materials, and equipment. Investment decisions must consider if today's investment will increase tomorrow's revenues enough to cover expenses. Investing is thus a commitment to monetary capital over time in anticipation of economic benefits. It directly relates to how businesses operate and how the values of shareholders are established [6]. Then there is the fact that, aside from services and product innovation, financial innovation is part of an organization's disruptive activities such as structure, operation, engagement, and distribution to attain sustainability, greater performance, and competitiveness. Recently, researchers have paid enough attention to the study of investment decisions [[7], [8], [9], [10], [11]]. Nonetheless, studies on the effects of innovation technology on investment decisions are still uncommon [12]. Financial Innovation plays a crucial role in attaining sustainability, and competitiveness, enhancing financial performance, and investment choices in dynamic marketplaces [6,13]. The linkage between fintech and bank performance has been studied worldwide, but our research is not focusing only on the above concepts. This research points to bridging the gap by analyzing the effects of financial innovation on investment decisions and considering the mediated link via stakeholders' satisfaction and banks' performance as well as the moderating effect of internet security. Also, we based our research on a sample of selected Congolese banks, where financial technologies and innovations influence many aspects such as the financial market, financial development, and organization performance. Moreover, it is highlighted that financial innovation contributes to 4% of the gross domestic product (GDP) in Congo [[14], [15], [16]]. With improved infrastructure, ICT is expected to contribute even more, particularly in the banking sector and service industries which it is already playing a significant role. Financial innovation is widely used in the Republic of Congo, and it has brought traditional financial services a new lease on life. According to the World Bank [15], Congolese banks are experiencing emerging technologies like mobile banking, internet banking, etc., which are contributing efficaciously to the overall country's development. Additionally, the electronic transaction market (mobile banking) has expanded dramatically, increasing financial development and accessibility of financial services [14,16,17].
This study examines the effect of financial innovation and stakeholders' satisfaction on investment decisions for banks operating in the Republic of Congo's expanding market. This research also aims to justify and support the Theory of Resource-based View (RBVT). The RBVT's main tenet is that an organization's competitive advantage is built on using its diverse assets and innovative skills. To this end, financial innovation is investigated for its contribution to global competitive advantage, and financial performance as well as the ease of investment decisions [1,12]. In contrast, academics have neglected the mediating impacts of stakeholders' satisfaction, and bank performance, and it is unusual to obtain a study of this sort in the financial industry. This study will provide answers to the observing concerns: How can stakeholders' satisfaction and bank performance mediate the relationship between financial innovation and investment decisions? Does internet security moderate the relationship? The study has diverse contributions that will benefit bank managers and owners in many ways. Firstly, bank executives must keep in mind that various components of innovation have varied cumulative consequences on investment decisions. As a result, they should spend greater funds on distinctive components depending on the investment functionality highlighted by their market projects and strategies. They should work harder to increase stakeholder satisfaction, especially if their businesses need to increase the rewards of investment. However, if the plan's top aim is to boost investment income, special consideration must be given to customer happiness and performance improvement measures. Also, with online transactions, current consumers can learn more about how to protect their accounts and identify security flaws. Moreover, this research offers a better understanding view of significant determinants, letting managers, owners, policymakers, and customers comprehend the significant effects of FI and IS in investment decision-making. This study further advances the understanding of how by improving internet security in dealing with financial innovation and customers' satisfaction, the firm may increase customers' loyalty and customers trust, and investment decisions. Studying the effects of financial innovation on investment decisions in the Republic of Congo is of paramount significance for several reasons. The specific context of the Republic of Congo, with its distinct economic, social, and regulatory conditions, provides insights that contribute to the broader understanding of banking dynamics, particularly in emerging markets. The research's comprehensive framework, analyzing direct effects as well as mediated links via stakeholders' satisfaction and banks' performance, coupled with the moderating effect of internet security, enriches the existing literature by offering a nuanced perspective on decision-making processes. This multifaceted approach holds practical implications for stakeholders, including bank managers, owners, policymakers, and customers in the Republic of Congo, allowing for informed decision-making, optimized strategies, and enhanced competitiveness. Moreover, the study directly addresses the country's economic development by uncovering the intricate relationships between financial innovation and investment decisions, offering valuable insights for policymakers seeking sustainable growth. Additionally, the research sheds light on the adoption of financial technologies in the region, providing crucial insights into the trajectory of technological adoption and contributing to the understanding of cybersecurity concerns in the digital era. In summary, the study's significance lies in its unique contribution to knowledge, addressing specific contextual factors and offering practical implications for stakeholders in fostering economic development, technological adoption, and ensuring the security of financial systems.
Previous studies examine how financial and investment decisions affect a variety of response factors, such as profitability, and economic growth [8,18,19]. While the current study examines the effect of fintech on investment decision, and the study also examines the impact of internet security as a moderator. Moreover, prior studies were conducted in other countries [11,20], whereas the current study focuses on the Congolese market, particularly in the banking industry. Alongside their significance in preventing insolvency, financial instability, and failure, financial innovations, and investment choices have gained great interest. The conclusions of this study can be applied to other industries. Customer satisfaction, employee satisfaction, and bank performance are defined as mediators that influence investment decisions. A mixed method was developed that relates financial innovation and investment decisions. Using primary data from 575 bank employees and customers to test our theoretical framework. To test the hypotheses, we employed Structural Equation Modelling. The data was analyzed using SPSS and SmartPLS 3.2.8.
The next section analyzes the theoretical underpinnings and development of theories. The third portion addresses the methods and collection of data. The fourth section presents the analysis of the findings. In the last part, we provide the discussions, conclusion, research implications, and future research opportunities.
2. Review of literature and hypothesis development
2.1. Overview of the banking ecosystem in Congo
The banking sector in the Republic of Congo has undergone significant evolution, reflecting the country's economic history and development. Since gaining independence in 1960, the Congolese banking system has evolved from its nascent stage with four commercial banks, namely Banque Commerciale Congolaise (BCC), International Bank for Trade and Industry (BICI), Société Générale des Banques du Congo (SGBC), and International Bank for West Africa (BIAO). Subsequently, the sector has witnessed liquidation, with notable banks like BCC and Banque Nationale de Développement du Congo (BNDC) facing economic challenges and ultimately dissolving. The 1990s marked a period of restructuring and privatization in response to various crises, leading to the emergence of restructured or privatized banks. Notable institutions include Union Congolaise de Banques (UCB), BGFI Bank, Banque de l’Habitat de Tunisie (BHT), Banque Espirito Santo Congo (BESCO), Banque Congolaise de l’Habitat (BCH), and others. The banking sector's liberalization and privatization peaked in the 2000s, resulting in a more diverse landscape of operational commercial banks. As of December 2022, the Republic of Congo has 11 operational commercial banks, showcasing growth in size and outreach. The sector's performance metrics indicate positive trends, with increasing numbers of branches, employees, and customers. The capitalization rate has steadily risen, reaching 15.34% by the end of 2022. Notable banks such as BGFI Bank and BSCA Bank control a significant portion of the total assets, reflecting a concentrated market structure. The Congolese banking system plays a crucial role in the country's economic landscape, contributing to 5% of GDP in 2022. The sector has experienced notable changes in deposits, credits, assets, provisions, and equity over the years. Despite fluctuations in these indicators, the net profit of Congolese banks reached XAF 20.9 billion in 2021, indicating financial resilience and profitability.
The banking industry's performance growth from 2012 to 2022 is graphically depicted in Fig. 1. The evolution of key metrics such as deposits, investments, provisions, and equity reflects the dynamic nature of the Congolese banking sector. The country's large unbanked population presents an opportunity for further growth and diversification of revenue streams, with only 17% of adults possessing a bank account in 2018, according to World Bank data [15]. In summary, the Republic of Congo's banking ecosystem has transformed over the years, adapting to economic challenges, embracing liberalization, and contributing significantly to the country's GDP and financial landscape. The sector's continued growth and performance underscore its importance in driving economic development and financial inclusion. Considering the aspect of financial innovation and investment decisions in the banking sector in Congo Brazzaville, it would be beneficial to delve into specific innovations, technological advancements, and their impact on banking services, customer experiences, investment strategies, and overall industry dynamics. Discussing how financial innovation has been embraced and its implications on the sector's growth and efficiency would provide a comprehensive overview.
Fig. 1.
Performance growth of Banks in the Republic of Congo.
2.2. Underpinning theory
The study on the “Effect of Financial Innovation and Stakeholders' Satisfaction on Investment Decisions: Does Internet Security Matter?” is grounded in the theoretical framework of the Resource-Based View (RBV). The RBV is a strategic management theory that posits that a firm's competitive advantage and sustained performance are driven by the unique and valuable resources it possesses. In the context of our research, we leverage the RBV to understand how specific resources, including financial innovation, stakeholders' satisfaction, and internet security, contribute to influencing investment decisions in the banking sector. Financial innovation, considered a strategic resource, aligns with the RBV perspective. Scholars such as Barney [21] and Yadav et al. [22], argue that strategic resources must be valuable, rare, inimitable, and non-substitutable (VRIN). Financial innovation, being unique to each organization, can offer a competitive advantage by creating new investment opportunities and altering risk-return profiles. Previous studies like that of Kruesi & Bazelmans [23], and Ployhart [24] emphasize the importance of unique resources in achieving competitive advantage. The RBV places significant emphasis on human capital as a valuable internal resource. In our study, stakeholders' satisfaction, comprising both employees and customers, is viewed as a critical dimension of human capital. Scholars like Delery & Roumpi [25], and Barney [21] highlight the role of human resources in shaping a firm's competitive advantage. Previous research, such as that conducted by Bakotić [26], links employee satisfaction to improved organizational performance. Investment decisions and overall bank performance are seen as outcomes influenced by the effective utilization of resources, as emphasized by the RBV. Barney [21] argued that the value of resources is determined by their impact on organizational performance. Previous studies, such as that of Sirmon & Hitt [27], illustrate how resource utilization influences firm performance. The RBV also underscores the importance of protective capabilities to safeguard valuable resources. Internet security, in our study, is considered a protective capability ensuring the secure and sustained use of critical resources. Previous research, including that by Hall [28], discusses how protective capabilities contribute to resource sustainability and competitive advantage. Previous studies applying the RBV to similar topics in the financial sector include work by Grimmer et al. [29], who explored the relationship between strategic resources and firm performance. Their results indicated that assets with a positive correlation to performance included informational resources, specifically business information systems, and access to financial capital. Additionally, studies like that of Mata, Fuerst, & Barney [30] investigated how unique resources contribute to competitive advantage. Conclusively, our research employs the RBV as a guiding theory to analyze the interplay between financial innovation, stakeholders' satisfaction, internet security, and investment decisions in the banking sector. The chosen constructs align with the RBV principles, providing a robust theoretical foundation for our study.
2.3. Review of literature and conceptual framework
Human capital (workers) and clients are two of the most valuable assets that firms must invest in, group, and consider to gain competitive advantages and increase overall productivity [31]. Utilizing workers is more considerable than obtaining them for the creations of values, and implementation of diverse strategies [32] as well as risks covering [31]. Investing is the placement of funds in a variety of financial assets or foundations with the hope of generating an uncertain return while taking into account the inherent risk involved in this process.
Nandini [23] described investing as a financial commitment undertaken with the anticipation of a return. The daily investment decisions that are undertaken by organizations or individuals such as banks result in future losses or profits [33]. Yet, not all investments are profitable as the investments’ decision makers do not behave or act sensibly all the time. Many factors, including technological advances, expertise, stakeholder satisfaction, competitive advantage, and sustainability, influence bank investment choices [16,17]. Fundamentally, everyone undertakes investment decisions in life at certain points, whether depositing or saving money, purchasing equipment, insurance, stocks, or constructing infrastructure, and every capital invested implicates risks taken [33].
Antony & Joseph [25] considered the decision to invest as a psychological processes as individual and corporations undertake decisions relying on the options available. Technical, basic, and natural psychoanalyses are all depending on the classic financial hypothesis that leads to rationality [11]. The individuals can purchase small quantities of shares for their accounts. Those investing in securities undertake this decision. According to Flor & Hansen [26], technological advancements influence a company's investment decisions as they affect investments' costs.
Many studies have typically focused on cash flow impacts on investment, the link between herdings and investments [34], corporates' investment and the confidence of CEO, efficacy, and investments forecasts [35], and volatilities and investments [36]. This study relies on the Resource-Based View Theory (RBVT). As stated by Anwar [6] a business model is a collection of unique corporate resources from which customers and organizational values can be created. The RBV theory supports the link between innovations and corporate effectiveness. Innovation, whether as a process or as an output, might be characterized as a vital resource that has the potential to serve as a source of ongoing competitiveness [37]. The RBVT's main tenet is that an organization's competitive advantage is built on using its diverse assets and innovative skills.
It is necessary to acknowledge that a company's resources will not generate positive benefits but rather its abilities or innovative capability to use resources effectively. In the same sense, financial innovation considers strategy, consumer expectations, future rival conduct, and customer attitudes [38,39]. Stakeholders are individuals, groups, or entities that have an interest or concern in the activities, decisions, or outcomes of an organization, project, or system. Stakeholders can be internal or external to the entity they are associated with, and they may include individuals, groups, communities, customers, employees, investors, government bodies, and other entities that can be affected by or affect the entity's actions and performance. The concept of stakeholders emphasizes the importance of considering various perspectives and interests to ensure responsible and sustainable decision-making. In our study, stakeholders are defined specifically as employees and customers. The selection of these two groups is grounded in their pivotal roles within the organizational context. Employees are considered crucial stakeholders due to their direct involvement in driving innovation. Their responsibilities encompass modifying, interacting, and developing ideas, making their satisfaction integral to fostering an environment conducive to innovation. Similarly, customers are regarded as essential stakeholders in any organization. In the contemporary landscape, surviving and thriving amid innovation and competition necessitate a continuous improvement of customer services and a serious consideration of their requests and complaints. The focus on these two stakeholder groups is strategic, aligning with the core principles that underscore the importance of internal innovation drivers (employees) and external factors shaping organizational success (customers). The selection of two variables is a deliberate choice aimed at maintaining focus and coherence in the study, allowing for an in-depth exploration of the intricate relationships between financial innovation, stakeholders' satisfaction, and investment decisions. Fig. 2 shows the study's conceptual framework.
Fig. 2.
Conceptual framework.
2.4. Research hypothesis development
2.4.1. Financial innovation (FI) and investment decisions (ID
The world is experiencing a time where the marginal advantages of innovative technologies still exceed the marginal expenditures associated with them. Financial innovation, characterized by the development and adoption of new financial products, technologies, and processes, has transformed the landscape of investment decisions. According to Tufano [40], financial innovation refers to the act of creating and then popularizing new financial instruments and technologies. In the context of this study, Financial Innovation (FI) refers to the introduction and adoption of new financial products, technologies, and processes within the banking sector. It includes advancements such as fintech platforms, algorithmic trading, crowd-funding, robo-advisors, and crypto-currencies. Financial innovation exerts a direct influence on investment decisions by ushering in novel investment opportunities and reshaping risk-return profiles. It's crucial to note that, in this context, investment decisions are not regarded as a total amount of investment but encompass a broader spectrum of strategic choices in allocating financial resources. For example, the emergence of robo-advisors, peer-to-peer lending platforms, and cryptocurrency exchanges has expanded the range of investment options available to individuals [41]. Investment Decisions (ID) in the context of finance refer to the choices made by individuals, firms, or financial institutions regarding the allocation of financial resources among various assets, projects, or opportunities with the expectation of generating returns. These decisions involve assessing potential risks, returns, and alternative investment options to maximize wealth or achieve specific financial goals. Different authors provide nuanced definitions that capture the essence of this concept. Myers & Majluf [42] expressed that Investment decisions pertain to the choices made by individuals and corporations regarding the acquisition or disposal of real or financial assets. Research into the impact of financial innovation on investment decisions has revealed intricate relationships. Palmié et al. [43], highlighted that innovative platforms such as robo-advisors have democratized investment access, making it more accessible to a wider demographic. Similarly, Ferretti et al. [44], demonstrated that crowd-funding platforms have enabled individuals to participate in funding startups, influencing investment diversification. A study by Demirgüç-Kunt & Levine [45], suggests that financial innovation, particularly the development of well-functioning financial markets and institutions, positively influences investment decisions, fostering economic growth. Research by Greenwood & Scharfstein [46], examines how financial innovation affects corporate investment. They find that innovations that ease financial constraints for firms positively impact investment decisions. The perceived benefits and risks of these innovations can influence how investors allocate their funds. Based on their findings, Hu & Xie [4] concluded that financial innovation might help banks to become more profitable. Also, some researchers suggested a link between IT and investment decisions. For example, Turedi & Zhu [47] obtained positive and significant moderating effects of information technology decision-making structural mechanism on the information technology investments and organizations' performance link. According to Božić & Botrić [8], the decision to invest appropriately in innovations is challenging due to a lack of resources and a variety of innovation options available to organizations. Similarly, they indicate that firms' decisions to invest in research and development (R&D) grow with sizes, markets' shares, and diversifications, as well as with demand-pull and technological push factors. Moreover, Hashi & Stojčić [34] highlighted that recipient firms invest more in innovations but produce less, calling into doubt the viability of existing national and EU policies for subsidizing innovation. Financial innovation encompasses a broad range of advancements, including the introduction of fintech platforms, algorithmic trading, crowdfunding, robo-advisors, and cryptocurrencies. Scholars have noted that these innovations have the potential to reshape investment strategies and risk profiles [43,48]. Pea-Assounga & Wu [17] found in another research that financial innovation is favorably and substantially associated with bank investment choices. In addition, they emphasized that the components of technologies and innovations have varied consequences on investment choices. In the same vein, Wu & Pea-Assounga [16] have indicated that financial innovation influences investment decisions as well as overall organizational activities. They concluded that policymakers have to improve the innovation and technologies services to be competitive and sustainable in their businesses. These studies collectively suggest that financial innovation can have positive effects on investment decisions, influencing economic growth, easing financial constraints for firms, improving risk-sharing, and fostering entrepreneurial activities. However, it's important to note that the effects may vary depending on the specific nature of the financial innovations and the broader economic context.
H1
Financial innovation would significantly influence investment decisions.
2.4.2. FI and Bank performance
Financial innovation, characterized by the introduction of new financial products, services, technologies, and processes, has significantly impacted the banking industry. With no other considerations taken into account, technology continues to outperform its marginal costs. According to Ref. [4], financial innovation has improved substantially efficiency, productivity, and overall success in the banking sector. Furthermore, a statistically significant perfect positive association was found between financial innovation and bank profitability [49,50]. Financial innovation encompasses a wide range of advancements, including the development of online banking, mobile payment systems, blockchain technology, and algorithmic trading. Scholars have highlighted that these innovations have reshaped how banks operate and engage with customers [14,51,52]. [1] demonstrated that the adoption of digital banking technologies can enhance operational efficiency, reduce costs, and improve customer satisfaction. Similarly, Lee et al. [40], found that banks embracing fintech solutions often experience improvements in transaction speed and accuracy. Innovation has a direct impact on customer engagement and satisfaction [53]. discussed how mobile banking apps have transformed the way customers interact with their banks, leading to increased customer loyalty. Conversely [54], highlighted the importance of addressing potential privacy concerns associated with innovative technologies. A study by Dwivedi et al. [43], found that Fintech adoption has a positive and significant impact on bank performance in UAE. The study showed that overall innovation and technology adoption in the UAE banking sector contributes to competitiveness and performance. Similarly, Tariq et al. [38], revealed that green innovation increases financial performance and economic growth. Several other scholars also confirmed that green innovation improves financial performance [55,56]. In the short term, however, this link may not be apparent because of high initial investment costs and the impact of environmental and social aspects on organizational change acceptability.
H2
Financial Innovation affects positively Bank Performance.
2.4.3. Relationship between FI and CS
A study by Nazaritehrani & Mashali [3] investigated the “Development of E-banking channels and market share in developing countries”. The findings of their study showed that online banking reduces bank operational expenses and intensifies client loyalty and retention. According to Amin [1], great customer satisfaction and retention are linked to the availability of financial innovation services and user-friendliness. The author added that financial innovation services lead to a substantial association between customers’ loyalty and satisfaction. Furthermore, Rahi et al. [2] argued that some banks have implemented financial innovation to save costs while increasing customer service and satisfaction. Similarly, Pea-Assounga & Wu [17] found that fintech has significant and positive effects on customer satisfaction. They further argued that financial innovation is beneficial for bank clients and facilitates them to access their accounts and make transactions all across the world.
H3
There is a significant positive link between Financial innovation and customer satisfaction.
2.4.4. FI and employees’ satisfaction
Consumers' expectations of financial services grew as technologies progressed. Even though some people still appreciate conventional banking, new technologies have improved banking products and services. Many people prefer utilizing posing devices and ATMs for shopping rather than waiting in bank halls [3]. Some financial technologies, such as financial innovation services, mobile banking, bank branches, and ATMs enhance employees' work and reduce their stress [17]. Typically, technology has a favorable effect on employee satisfaction. Most financial innovation services affect banking services, banks' activities, and work satisfaction as well as overall productivity. Similarly [57], stated that innovative technologies have greatly enhanced the banking system. In the same vein, Pea-Assounga & Wu [17] have also demonstrated that fintech influences positively employees’ satisfaction and it helps employees to accomplish their tasks quickly and reduces stress at the workplace. Another study by Ko & Choi [58] argued that firm innovation has a positive correlation with employee satisfaction. They further stated that employees who feel that coworkers refrain from acting advantageously are more likely to put in longer voluntary hours and engage in cooperative conduct, which increases organizational trust and productivity.
H4
Financial innovation affects positively ES.
2.4.5. Bank performance and investment decision
This section aims to synthesize existing research on the interplay between bank performance indicators and investment decisions across different contexts. The foundation of the relationship lies in understanding the indicators that signify bank performance. Scholars have identified several key indicators, including profitability, asset quality, capital adequacy, liquidity ratios, and efficiency measures. Empirical studies have demonstrated that these indicators collectively reflect a bank's financial strength and stability [59,60]. Research investigating the impact of bank performance on investment decisions has revealed nuanced findings [61]. found that during periods of robust bank performance, individuals and businesses exhibit higher confidence in borrowing and investing. Conversely, during times of bank distress, as shown by Ref. [62], there is evidence of reduced investment appetite due to concerns about credit availability and systemic stability. The availability of credit from banks is a critical determinant of investment decisions for businesses [63]. highlighted that when banks are performing well and have high liquidity, businesses are more likely to secure financing for investment projects. This availability of credit affects the timing and scale of investment activities. On the other hand, interest rates set by banks significantly influence investment choices. Research by Ref. [64] demonstrated that lower interest rates incentivize borrowing for investments, whereas [65] suggested that higher rates might lead to a slowdown in investment activity. Moreover, Bank performance shapes investor perceptions of risk [66]. observed that a bank's stable performance positively influences investor confidence and encourages engagement in investment activities. However, during financial crises, as [67] noted, concerns about bank stability can trigger risk aversion and drive investors towards safer assets. Furthermore, Bank performance affects broader market sentiment. Positive performance indicators contribute to a favorable market environment, leading to increased investment across various asset classes [68]. Additionally [69], underscored the role of banking regulations in shaping bank performance and consequently impacting investment decisions.
Educating and persuading stakeholders to make choices is an economic case for the firm that needs the accumulation, and usage of resources and indicates the organization's capacity to handle and monitor its capital. The financial ratios are the greatest approach to gauging a firm's financial success over time [70,71]. Al-Slehat [57] and Dang et al. [72], defined a set of ratios to evaluate the financial performance of organizations, including returns on assets, returns on equity, and returns on investment (RI). Many investors will undoubtedly buy bonds. They should also be conscious of stocks' values because they determine predictable future earnings. Moreover, the shareholder has to be aware of various aspects affecting stocks' prices, such as financial data [73]. Makarim & Ana [59] studied the financial performance of businesses. They showed that shareholders' financial advantage could be employed for decision-making on investments.
H5
Bank Performance affects positively Investment decisions.
2.4.6. CS and investment decisions
“How customer satisfaction leads to great capital investment and the decision to invest?” First, as CS comprises both novel consumers' views and knowledge of the qualities of businesses' products and services, great consumer satisfaction would result in extremely foreseeable revenue movements and potential possibilities for financial performance and growth. Fornell et al. [60] illustrate that high client expenditures and high potential demand are the product of customer satisfaction. Therefore, companies with high customer satisfaction have a larger potential for cash flows. In addition, a stable and loyal customer database is developed via the customers’ satisfaction and trust [74], reducing future cash flow volatilities and the costs of capital. Nonetheless, customer satisfaction increases customer loyalty, improves organizational values, reduces price elasticity and possible transaction costs, and improves employee performance and productivity [75]. In this vein, great customer satisfaction, regarding “neoclassical investment theory” offers inducements for businesses to increase their market businesses by investing greatly in resources. Overall, this research argues that customer satisfaction is a key factor influencing the investment strategy of the business.
H6
There would be a significant positive link between CS and investment decision.
2.4.7. ES and investment decisions
Human resources (HR) are the most valuable assets in any firm [76]. HR is the most important economic, cultural, and social growth pillar. Work satisfaction and motivation directly affect employee productivity. In other words, workers' evaluations of their employers are vital to their success [77]. Research by Bai et al. [65] demonstrates that limiting the right of employers to fire workers can have two opposing effects on investments. Isolating employees from unfair termination and the fear of being fired, on the one hand, can help them concentrate on their duties, and take risks for creativity and skills development that boost the effectiveness of their present workplace [14]. These effects may result in increased profitability as well as new or more appealing opportunities, leading to increased investments [16]. As businesses prepare for increased irreversibility investments, enhancing the cost of labor transfer declines resource expenditure [17].
H7
ES has a significant positive association with ID.
2.4.8. Direct and moderating effects of internet security
The internet has increased global connectivity during the last decade. Internet security is a subset of computer security that focuses on protecting online transactions. Internet security has become a critical consideration in today's digital landscape, particularly in the context of investment decisions. Internet security prevents attacks on networks, browsers, applications, and other operating systems [78]. Nowadays, governments and businesses are more concerned about cyber-attacks and malware programs. The fundamental goal of internet security is to establish specific norms and guidelines to deflect internet attacks. The increasing reliance on digital platforms for investment activities underscores the importance of robust internet security measures. Some scholars emphasize that a secure online environment is essential to building investor trust and facilitating engagement in investment activities [[79], [80], [81]]. Research has explored how perceived internet security affects risk perception among investors [82]. found that individuals are more likely to invest online when they trust the security measures in place. Conversely [83], noted that concerns about online fraud and data breaches can lead to heightened risk aversion. Internet security plays a critical role in preventing identity theft and financial fraud [84]. discussed the role of multi-factor authentication and encryption in safeguarding investors' personal and financial information. In the same vein [85], highlighted the importance of educating investors about phishing attacks and other cyber threats.
Yet probably and more importantly, the field of prospective trading partners has expanded dramatically, creating enormous new profits from an exchange [78,86]. However, like the real world, the internet has threats and cyber-criminalities. Due to the negative impact on the integrity confidentiality and privacy of banks and their clients, security challenges have grown more widespread in Internet technology. IT-based financial transactions appear to be dangerous for customers, as criminal operations can be carried out easily without physical interactions [5]. Consequently, because of privacy and security issues, most consumers are hesitant to embrace financial innovation services [2]. Since many banking services are now available via smart devices and the internet, consumers are deeply concerned about security problems. Empirical studies specifically exploring the moderating effects of Internet security on the relationship between financial innovation and investment decisions are scarce, extant research has examined the moderating effects of related factors. For instance Ref. [80], investigated the moderating effect of security and privacy issues on the relationship between trust and online purchasing. They found that higher security and privacy strengthened the positive relationship between trust considerations and online purchasing decisions. From available studies, customers' concerns about safety and security are the main deterrent to using financial innovation [5,81,87]. Therefore, privacy and security are extensively the major issues that hinder financial innovation acceptance among customers. To solve these issues banks always take precautions by investing in security projects and financing the programs that straighten the protections of their banks’ systems.
H8a
Internet security may influence investment decisions.
H8b
Internet security may moderate the relationship between FI and ID.
2.4.9. The mediating role of BP on the nexus between FI and ID
Gill et al. [75] refined that individuals and institutional investors make decisions relying on their objective perspectives, knowledge, and financial information available. Investing decisions take into account financial and operational aspects, stock growth expectations, profitability, and liquidity. Type of investment, project value, risks, and limited financial capital availability all impact decision-making [88]. To become exceptionally profitable, the firm's financial and investment decisions must be accurately and timely evaluated [71]. This efficiency displays managers' ability to utilize assets to obtain and improve future benefits. We argue that, given the above theoretical foundations and findings, and the fact that FI may be a precondition for organizational innovations and investment decision-making, BP can assist in assessing ID.
H9
BP mediates the nexus between financial innovation and ID.
2.4.10. The mediating roles of CS and ES on FI and ID relationship
As per “Neoclassical Investment Theory”, a company's expected returns on investments and capital cost influence its investment decisions. Tobin's method compares the companies' Q (marginal markets' values of asset units) with other marginal costs of investments. In situations when Tobin's Q is low or where growth potential is high, organizations can afford to spend more. Since the market is not frictionless, a company's investment options are more complex. Consequently, a company's investment plan is heavily reliant on cash flows [16]. Companies can invest more when they are more efficient. However, many models show that expenditure levels are linked to cash flow. Sorescu & Sorescu [77] revealed that investment in organizations with high consumer satisfaction yields large earnings with insignificant risks. Merrin et al. [89] have shown that customer satisfaction may be utilized to assess stock price fluctuations. The banks assist customers and employees in reducing functioning expenses, inefficiencies, and risks while increasing customer loyalty and employee performance as well as overall productivity [90]. Pea-Assounga & Wu [17] indicated that fintech influences both employee and customer satisfaction, which ultimately leads to banks' investment decisions. They concluded that stakeholders' satisfaction is a significant predictor of banks' investment decisions as it assists the banks to enhance financial assets and predicts their financial performances which finally helps to decide where and when to invest.
H10
CS will mediate the link between FI and ID.
H11
ES does mediate the link between financial innovation and ID.
3. Data collection and methodology
3.1. Data collection
This paper uses qualitative and quantitative approaches to gather and examine the study data. Structured surveys were used to quantify the constructs. The data came from eleven (11) banks licensed to operate in the Republic of Congo. The study comprises 375 customers and 225 employees. The bank's staff and customers were chosen using a combination of purposive and random samplings. These approaches ensured that at least the top 10 managers of each bank were selected. The sample for bank staff includes top-level managers and customer service representatives who are responsible for managing these banks' branches and can provide adequate operations information. The combination of purposive and random sampling was employed to ensure both representation of top-level managers (purposive) and a broader spectrum of participants (random). Purposive sampling targeted key decision-makers, essential for obtaining managerial insights, while random sampling contributed to a more diverse participant pool, enhancing the study's external validity and generalizability. The questionnaires have been translated in French and backward to English to keep proper interpretations and convey intended meanings. The data was gathered between November 2022 and March 2023. The data sample size was selected utilizing Westland's [91,92] method that determines the indicator-to-latent variable ratio (r) and computing the sample size lower limit. Using this criterion, the minimum sample size needed must confirm the function “.” The r can be computed as “ “, where p denotes the “number of items and k is the number of constructs”. After processing, p = 28; k = 6; r = 4.67 and . We have found that , which implies that our sample size is sufficient and consistent for accurate analysis [91]. The valid questionnaires collected and examined are 575 samples comprising 215 employees and 360 clients out of 600 participants originally targeted, representing 95.83% of the response rate. In accordance with ethical guidelines and institutional regulations, informed consent was obtained from all participants involved in this study through a written consent form.
We used assumption tests to ensure that the two groups (workers and customers) had similar variances and equal means and that they were not significantly different. To evaluate if the population incorporated in the sample size framed is homogeneous, the “Homogeneity of Variance Test”, also called “Levene's Test” is utilized [93]. The testing results demonstrated the absence of statistical differences concerning the two different groups with the “Levene's Statistic” for the dependent variable, ID, F (1, 573) = 0.567; P = 0.452, implying that the equality of variances assumption is verified. On the other hand, The Test of Brown-Forsythe supported the robustness of Levene's test with Statistic = 1.607; P = 0.205 for ID [94]. Additionally, the mean values for staff and customers were 13.94 and 14.10, respectively. This suggests that our research validates (i.e., does not violate) the “homogeneity of variances and equal means” assumptions. Financial innovation (FI) in our study is quantitatively assessed through survey responses gathered from banks' employees and customers. We consider various dimensions of financial innovation, including the introduction of new financial products, technological advancements, and improvements in risk management practices. Quantitatively, we measure FI by assigning numerical values to responses related to the frequency and impact of innovative financial products and services introduced by the banks. Respondents rate the extent to which their banks have implemented innovative practices, allowing us to derive a quantitative measure of financial innovation. On the other hand, investment decisions (ID) are quantitatively operationalized based on the responses obtained from survey participants regarding their banks' allocation of financial resources. This includes investments in different financial instruments, projects, and portfolios. Respondents provide quantitative feedback on the magnitude and nature of investment decisions made by their banks during the specified period.
The relationship between financial innovation (FI) and investment decisions (ID) is analyzed quantitatively by employing statistical methods such as correlation analysis and structural equation modeling. We explore how the perceived level of financial innovation, as reported by respondents, correlates with the quantitative measures of investment decisions. This involves assessing the impact of financial innovation on the allocation of funds, the diversity of investment portfolios, and other relevant quantitative aspects of investment decision-making. Given that our data is collected through questionnaires, our quantitative approach aligns with similar studies conducted in various parts of the world. We acknowledge the importance of robust statistical analysis to establish a clear and quantitative understanding of the relationship between FI and ID, and we ensure that the methodology section of our paper has provided explicit details on the statistical techniques employed for this purpose.
3.2. Constructs measurements
This research utilized a “5-point Likert scale”, with 1 designating “strongly disagree” and 5 implying “strongly agree”, and also examined relevant questionnaires derived from other researchers that carried out similar investigations. We have modified the items of this paper from other investigators. The Financial Innovation items were taken from Ref. [2], the employee satisfaction items were taken from Yee et al. [83], and the customer satisfaction items were adopted from Hammoud et al. [61]. The bank performance items were taken from Rahi et al. [2], and the internet security items were adopted from Aboobucker & Bao [5]. Finally, the investment decisions items were obtained from Ogunlusi & Obademi [11]. The study constructs have been chosen from the theory of the resource-based view (RBV) through a careful examination of the literature and theoretical frameworks relevant to the RBV. The RBV suggests that a firm's competitive advantage and performance are driven by the unique resources and capabilities it possesses. In our study, the chosen constructs namely, financial innovation, stakeholders' satisfaction (comprising consumer and employee satisfaction), bank performance, investment decisions, and internet security are aligned with the RBV framework. Financial innovation represents a strategic resource that can influence a firm's competitive position, while stakeholders' satisfaction reflects the internal resource of human capital, crucial for innovation and performance. Bank performance and investment decisions are outcomes influenced by the effective utilization of these resources, and internet security is considered as a critical capability ensuring the protection of valuable resources.
3.3. Basis of selecting “SEM (Structural Equation Modelling)” approach
SEM is a strategy that simultaneously handles several social science issues and explores the links between components using diagrams. Utilizing techniques such as factor analysis, path analysis, and regression, SEM has also been used to evaluate the model's fit [95]. This method seems more statistically accurate than other previous methods [96,97]. Utilizing SEM to assess causal effect models with latent constructs has turned out to be a common method of analysis. CB SEM and PLS-SEM are logically distinct [98,99]. The CB SEM approach is appropriate for theory testing and confirmation. To make predictions and construct theories, PLS-SEM is the best way. However, maximizing explained variance in dependent constructs is also an objective. To test and confirm the existing hypothesis, CB-SEM is the best choice. However, PLS-SEM is better for forecasting and theory development [98]. PLS-SEM is the preferred method for gaining comprehensive knowledge on the indicators of customer happiness, brands image, or business reputation. For several scientists, PLS-SEM and CB-SEM literally identical [100]. Empirical methodologies in businesses are used for prediction and explanations [101]. The use of CB-SEM often ignores a major purpose of investigations and predictions [100]. The answer to this fundamental flaw is PLS-SEM, which aims to forecast endogenous latent variables. PLS-SEM has some advantages over CB-SEM. numerous analysts only acknowledged the “distributional assumptions” of the variables. Indeed, most empirical business and social sciences data are non-normal [[98], [99], [100], [101]]. Most CB-SEM users neglect the characteristics of this method's criteria and violations. PLS-SEM avoids this stringent “distributional assumptions”, making it a feasible option than a CB-SEM approach. As our study emphases on testing the theoretical framework from prediction perspectives and the model is complicated and comprises lots of indicators, constructs, and/or model relationships, it is suitable and appropriate to apply PLS-SEM.
3.4. Confirmatory Factor Analysis (CFA)
To ensure the reliability of the data, both SmartPLS 3.2.8 and SPSS were employed to conduct tests for convergent and discriminant validity. For the identification of components with eigenvalues exceeding one, Principal Component Analysis (PCA) with varimax rotation was utilized. During the data reduction process, items with factor loadings equal to or greater than 0.500 were scrutinized for analysis [102]. The outcomes of the Confirmatory Factor Analysis (CFA) on research items unveiled a six-factor solution that accounted for 68.52 percent of the total variance across all research constructs. The CFA exhibited favorable values with KMO = 0.879, Bartlett's significance = 0.000, and a Scree plot trend showing a decline by a factor of seven.
3.5. Measurements reliability and validity
The reliability of the constructs was assessed using “Cronbach's Alpha and composite reliability”.
Cronbach's alpha measures the internal consistency or scale. The results showed a Cronbach's Alpha of 0.701 into 0.868 which is over the satisfactory span of 0.70. The “composite reliability” ranges from 0.821 to 0.932, which is significantly higher than the critical value of 0.70, indicating that the constructs are quite reliable [91]. The SmartPLS EFA results show that 28 items' loading values are ranging between 0.645 and 0.952, which are acceptable regarding the limit of 0.70 (see Fig. 3), indicating that each item or indicator is strongly loading in its assigned construct. This reveals that the items utilized are adequate and reliable [96,97].
Fig. 3.
Output of study model from SmartPLS.
The extracted average variances (AVE) were checked for convergences. The AVE ranges from 0.587 to 0.694 for this research, which is satisfactory and above the critical threshold of 50%. All reliability and validity findings are displayed in Table 1. The predicted models' “discriminant validity” is also evaluated by employing the criterion of “Fornell-Larcker.” The hypothetical model's correlations of constructs must be less than the AVEs square root to meet the “Fornell-Larcker criterion” [97]. Table 1 indicates that the outcomes of the assessment meet the criteria of “Fornell-Larcker.”
Table 1.
Discriminant Validity (Fornell-Larcker Criterion) and constructs reliability.
| (1) | (2) | (3) | (4) | (5) | (6) | CA | CR | AVE | |
|---|---|---|---|---|---|---|---|---|---|
| (1) BP | 0.813 | 0.868 | 0.906 | 0.661 | |||||
| (2) CS | 0.470 | 0.833 | 0.851 | 0.900 | 0.694 | ||||
| (3) FI | 0.445 | 0.384 | 0.771 | 0.863 | 0.898 | 0.594 | |||
| (4) ES | 0.529 | 0.372 | 0.429 | 0.767 | 0.802 | 0.850 | 0.588 | ||
| (5) ID | 0.701 | 0.534 | 0.415 | 0.620 | 0.766 | 0.859 | 0.895 | 0.587 | |
| (6) IS | 0.479 | 0.439 | 0.363 | 0.360 | 0.662 | 0.778 | 0.701 | 0.821 | 0.605 |
Note: CA – Cronbach's Alpha, CR- Composite Reliability, and AVE –Average Variance Extracted. Underlined and bold Values are the square root of AVE.
We used descriptive statistics to analyze the variables' means, standard deviation, and correlation. A positive substantial correlation was found between the research variables, with values varying between 0.250 and 0.771. Table 2 shows no value of correlation exceeding 0.9, indicating the absence of common-methods-bias between the study constructs [103]. Moreover, the correlation analysis assists in understanding all non-recommended and suggested links between variables.
Table 2.
Constructs means, Std. Deviation, and Correlations.
| Variables | Mean | Std. Deviation | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|---|---|
| (1) BP | 17.18 | 4.51 | 1.000 | |||||
| (2) CS | 13.62 | 3.18 | 0.412** | 1.000 | ||||
| (3) FI | 19.67 | 4.91 | 0.422** | 0.288** | 1.000 | |||
| (4) ES | 14.11 | 2.96 | 0.426** | 0.270** | 0.362** | 1.000 | ||
| (5) ID | 20.10 | 4.74 | 0.771** | 0.455** | 0.390** | 0.460** | 1.000 | |
| (6) IS | 9.62 | 2.33 | 0.327** | 0.253** | 0.263** | 0.250** | 0.478** | 1.000 |
Note: ** Significant of the correlation at the 1% level (2-tailed).
3.6. Test of common methods bias
As both exogenous and endogenous constructs data have been collected exploiting the same surveys, it is required to perform the common method bias evaluation [103]. To examine our research's variables, “Harman's single-factor test” is utilized. The outcomes proved that the single component merged explained around 34 percent of the overall variance for the models, that is less than the critical threshold of 50% [103]. Also, the results in Table 3 show the VIF values to evaluate the multi-collinearity between constructs. The values of VIF for items and variables are also less than the limit value of five [102,103]. Thus, there is no multicollinearity issue.
Table 3.
VIF values.
| Items | Outer VIF Values | Inner VIF Values |
||||||
|---|---|---|---|---|---|---|---|---|
| BP | CS | FI | ES | ID | IS | |||
| Bank Performance (BP) | 1.767 | |||||||
| BP1 | 2.189 | |||||||
| BP2 | 2.760 | |||||||
| BP3 | 2.962 | |||||||
| BP4 | 2.307 | |||||||
| BP5 | 1.256 | |||||||
| Customer Satisfaction (CS) | 1.448 | |||||||
| CS1 | 2.365 | |||||||
| CS2 | 1.971 | |||||||
| CS3 | 2.054 | |||||||
| CS4 | 2.003 | |||||||
| Financial Innovation (FI) | 1.000 | 1.000 | 1.000 | 1.407 | ||||
| FI1 | 2.730 | |||||||
| FI2 | 3.288 | |||||||
| FI3 | 2.661 | |||||||
| FI4 | 2.485 | |||||||
| FI5 | 2.220 | |||||||
| FI6 | 3.278 | |||||||
| Employee Satisfaction (ES) | 1.513 | |||||||
| ES1 | 1.072 | |||||||
| ES2 | 2.167 | |||||||
| ES3 | 2.659 | |||||||
| ES4 | 3.076 | |||||||
| Investment Decision (ID) | ||||||||
| ID1 | 2.845 | |||||||
| ID2 | 3.511 | |||||||
| ID3 | 2.179 | |||||||
| ID4 | 2.215 | |||||||
| ID5 | 3.319 | |||||||
| ID6 | 1.807 | |||||||
| Internet Security (IS) | 1.441 | |||||||
| IS1 | 2.098 | |||||||
| IS2 | 1.125 | |||||||
| IS3 | 2.192 | |||||||
The measurement items' fit indices, which include SRMR, d_ULS, d_G, X2, and NFI, were also evaluated. All fit indices were acceptable, as shown in Table 4.
Table 4.
Model fit summary.
| Measures | Saturated Model |
|---|---|
| SRMR (Standardized Root Mean Square Residual) | 0.053 |
| d_ULS (Squared Euclidean Distance) | 0.347 |
| d_G (Geodesic Distance) | 0.241 |
| X2 (Chi-Square) | 1186.84 |
| NFI (Normed Fit Index) | 0.971 |
4. Results
The questionnaires yielded significant respondents’ demographic profile information including education level age, gender, and level of monthly income.
The outcomes in Table 5 reveal that 291 respondents are males rating 50.6%, and 284 participants representing 49.4% are females. 149 of the participants representing 25.9% are in the age group of 20–24 years. 196 respondents rated 34.1% belonging to the age group of 25–30 years; 134 participants representing 23.3% are between the ages of 31–40 years; 96 participants comprising 16.7% are related to the group age of 41 and above. The results in Table 5 reveal that 43 of the participants comprising 7.5% are from primary school; 101 respondents rating 17.6% are from the intermediate level or high school while 128 respondents representing 22.3% have a diploma; 180 of the participants rating 31.3% have the bachelor degree while 123 of the participants rating 11.4% are from the graduates level. Moreover, Table 5 shows the statistics linked to the monthly income (in XAF/FCFA) of the participants. The findings indicate that 47 of the participants comprising 8.2% obtain a monthly income of up to 95 thousand. Around 23% of respondents get an income between 96 and 155 thousand per month while 131 participants representing 22.8% have a monthly income belonging to the range 156–205 thousand; 107 of the participants representing about 18.6%, their monthly salary ranges between 206 and 310 thousand while 82 of the participants comprising 14.3% gain per month income belonging to the group 311 and 500 thousand. Lastly, 14% of the participants receive a monthly salary of 501, and higher. The demographic profile (Table 5) provides valuable insights into the characteristics of our sample. Notably, the distribution of participants across gender, age groups, education levels, and monthly income brackets showcases the diversity within our surveyed population. This information serves as a foundational understanding for subsequent analyses, shedding light on potential variations in stakeholders' perspectives.
Table 5.
Demographic profile information of participants.
| Participants (No. = 575) |
||
|---|---|---|
| Frequency | Percent | |
| Gender | ||
| Male | 291 | 50.6 |
| Female | 284 | 49.4 |
| Age (in years) | ||
| 20-24 | 149 | 25.9 |
| 25-30 | 196 | 34.1 |
| 31-40 | 134 | 23.3 |
| 41 and greater | 96 | 16.7 |
| Education Level | ||
| Primary school | 43 | 7.5 |
| Intermediate Level or High School | 101 | 17.6 |
| Diploma level | 128 | 22.3 |
| Bachelors | 180 | 31.3 |
| Graduates (Master and Ph.D.) | 123 | 11.4 |
| Monthly Income (in FCFA/XAF) | ||
| Up to 95,000 | 47 | 8.2 |
| 96,000–155,000 | 130 | 22.6 |
| 156,000–205,000 | 131 | 22.8 |
| 206,000–310,000 | 107 | 18.6 |
| 311,000–500,000 | 82 | 14.3 |
| 501,000 and more | 78 | 13.6 |
No. Of participants = 575.
4.1. Hypothesis testing
The impact of financial innovation on investment decisions was tested utilizing the statistical software SmartPLS3.2.8. The outcomes of this research revealed a substantial association between the studied dependent and the exogenous variables.
4.2. The direct effect of FI on ID, CS, ES, and BP; and the effect of CS, ES, BP, and IS on ID
To assess the direct effects of financial innovation (FI), Stakeholders’ satisfaction (customer satisfaction (CS), and employee satisfaction (ES)), bank performance (BP), and internet security (IS) on investment decisions (ID), we employed PLS-SEM. The findings demonstrated that standardized coefficients in Table 6 from FI to ID, BP, CS, and ES are −0.062 (p < 0.01), 0.445 (p < 0.01), 0.384 (p < 0.01), and 0.429 (p < 0.01), respectively. Therefore, H1, H2, H3, and H4 were supported. Also, the direct effects of internet security, BP, CS, and ES on ID were significant, this implies that the above-mentioned constructs are relevant predictors of investment decisions. Therefore, the hypotheses H5, H6, H7, and H8a were likewise confirmed (See Table 6, Figs. 3, and Fig. 4).
Table 6.
Bootstrapping outputs; “direct, indirect, and total effects”.
| “Direct effects” | |||||
|---|---|---|---|---|---|
| Path | Std. Coef. | St. Err. | z | P > z | [95% CI] |
| FI - > ID | −0.062 | 0.014 | −4.316 | 0.000 | −0.090–−0.035 |
| FI - > BP | 0.445 | 0.026 | 16.990 | 0.000 | 0.396–0.496 |
| FI - > CS | 0.384 | 0.026 | 14.579 | 0.000 | 0.329–0.436 |
| FI - > ES | 0.429 | 0.023 | 18.497 | 0.000 | 0.385–0.475 |
| BP - > ID | 0.512 | 0.018 | 28.891 | 0.000 | 0.476–0.547 |
| CS - > ID | 0.093 | 0.016 | 5.739 | 0.000 | 0.061–0.125 |
| ES - > ID | 0.227 | 0.016 | 14.020 | 0.000 | 0.196–0.259 |
| IS - > ID | 0.316 | 0.015 | 20.542 | 0.000 | 0.285–0.346 |
| FI*IS - > ID | −0.016 | 0.010 | −1.600 | 0.136 | −0.040–0.001 |
| “Indirect Effects” | |||||
| FI - > ID | 0.362 | 0.018 | 19.667 | 0.000 | 0.326–0.396 |
| “Total effects” | |||||
| FI - > ID | 0.300 | 0.022 | 13.512 | 0.000 | 0.258–0.343 |
| FI - > BP | 0.445 | 0.026 | 16.990 | 0.000 | 0.396–0.496 |
| FI - > CS | 0.384 | 0.026 | 14.579 | 0.000 | 0.329–0.436 |
| FI - > ES | 0.429 | 0.023 | 18.497 | 0.000 | 0.385–0.475 |
| BP - > ID | 0.512 | 0.018 | 28.891 | 0.000 | 0.476–0.547 |
| CS - > ID | 0.093 | 0.016 | 5.739 | 0.000 | 0.061–0.125 |
| ES - > ID | 0.227 | 0.016 | 14.020 | 0.000 | 0.196–0.259 |
| IS - > ID | 0.316 | 0.015 | 20.542 | 0.000 | 0.285–0.346 |
| FI*IS - > ID | −0.016 | 0.010 | −1.600 | 0.136 | −0.040–0.001 |
Note: FI*IS, Moderating Effect, CI, Confidence Intervals.
Fig. 4.
PLS bootstrapping output.
4.3. Mediation analysis
Table 6, Table 7 demonstrate the indirect effect of FI on the dependent construct, investment decisions; and this effect is mediated through BP, CS, and ES.
Table 7.
“Specific indirect effects”.
| Std. Coef. | St. Err. | z | P > z | [95% CI] | |
|---|---|---|---|---|---|
| FI - > BP - > ID | 0.229 | 0.016 | 13.809 | 0.000 | 0.198–0.262 |
| FI - > CS - > ID | 0.036 | 0.007 | 5.328 | 0.000 | 0.023–0.049 |
| FI - > ES - > ID | 0.097 | 0.007 | 14.561 | 0.000 | 0.085–0.110 |
Note: Std. Coef., Standardized coefficients; St. Err., Standard errors.
Table 6 proves that the overall indirect effect of FI on investment decisions via the mediators is statistically and positively substantial (β = 0.362, p < 0.01). These findings underscore the importance of considering not only the direct impact of financial innovation but also the nuanced pathways through which it influences stakeholders and subsequently shapes investment decisions. Particularly, the specific indirect effects reveal that BP, CS, and ES all mediate the relationship (see Table 7). Along such terms, it is reasonable to conclude that the overall mediation effects were significant statistically, indicating that H9, H10, and H11 were likewise accepted. The moderating role of internet security has also been examined in this study. The result proves that IS does not moderate the link FI - ID, as the coefficient was not statistically significant (see Table 6 and Fig. 3). Therefore, the H8b was not supported as in our research.
5. Discussion and conclusion
Financial institutions use financial innovation to help consumers with financial transactions and to reduce banks’ halls waiting time. Increasing financial innovation usage and trust reduces transaction costs and enhances customer loyalty, competitive advantage, and business performance. This study finds that financial innovation is strongly linked to stakeholders' satisfaction, bank performance, and investment decision. Also, IS, bank performance, and stakeholders' satisfaction (SS) are all significantly linked to investment decisions. This assessment of financial innovation and internet security predicts SS and ID. Customer satisfaction, BP, and ES indirectly predict ID. This research contributes significantly to the literature characterized by an absence of works linking FI, SS, BP, IS, and ID within financial institutions. Also, the insights can help suppliers of financial innovation services to better understand and control prior factors of investment decisions and competitive advantage. This study adds to the debate about internet security in online services management. To improve the effectiveness of financial innovation strategies or plans, financial organizations' managers must understand how internet security-related concerns affect the perception of financial innovation dimensions.
In the context of Congo, internet security is highly tied to ID but fails to moderate FI-ID. This implies that Congolese bank managers do not have to base their investment decisions on internet security. However, managers should consider Internet security when upgrading or supplying financial innovation services to customers because it is directly associated with online banking. Also, when they focus on online services and payments. This supports the view of Rahi et al. [2] that internet security is vital for financial institutions that rely on online services and transactions. Our results support the claim made by other research that the choice of an investing strategy relies on several variables [8,58]. Additionally, we obtained that various components of innovation and technology had diverse impacts on investment choices. FI in particular has a huge influence on banks' investment choices, both directly and indirectly. Precisely, FI has a negative and significant effect on ID. This outcome is consistent with the claim made by Hashi & Stojčić [34] that the use of resources and the information gained via innovations influence business choices. The results are in line with those of Pea-Assounga & Wu [17] showing a strong and positive relationship between fintech and ID. Furthermore, this result is consistent with Flor & Hansen's [26] conclusion that technology advancements have an impact on a firm's investment choices. Similarly, BP, CS, and ES mediated the link between FI and ID. These outcomes indicate that concentrating on competitive advantage, performance, customers, and workers should be considered to ensure the profits expected from banks' decisions. Also, CS, BP, and ES are essential factors in promoting ID. Companies that have strong technological innovations can develop successful connections with a variety of stakeholders, resulting in more market opportunities and improved profitability. Briefly, these findings add to the body of knowledge on innovations by highlighting important mechanisms through which elements of innovation aid banks in making better judgments. They further add to the human resources management (HRM) works by recommending which FI components a company must prioritize. Our results support the argument that various factors may mediate the effect of innovative technology on investment decision. Despite this, our study is one of the first to investigate SS, and BP as mediators in a single study. It explains the FI-investment decisions relationship in another way. As shown in Table 6, Table 7, and Fig. 3, internet security, ES, and BP have a greater impact on bank investments decision than CS. These findings add to the present HRM and innovation literature and can help organizations establish a comprehensive human resource process to enhance financial innovation, stakeholders' loyalty, and investment decisions. Our findings are inconsistent with Giordani & Floros [92], which highlighted a substantial and positive correlation between the use of financial innovation services by Greek banks and lower operating costs. Their results also revealed that the financial performance of the Greek banks appears unaffected by Internet banking services. Additionally, the results of our study concur with those of Carboni & Medda [20], who revealed a favorable and substantial relationship between innovation activity and investment choices. This indicates that, given the tight connection between the accumulation of physical capital and technical improvement, the interaction between innovations and investment choices is crucial for a firm's success. Furthermore, our results are in line with those of Dwivedi et al. [43] who found that the use of fintech has a good and substantial effect on the performance and competitiveness of banks in the United Arab Emirates (UAE).
Indirect outputs demonstrate that BP partially mediates the financial innovation and investment decisions relationship. This confirms the study's hypotheses. According to innovation theory, the more innovative firm has greater financial controls and self-investment [104]. Decisions based on innovation may boost future profitability. This finding supports Abdin et al. [94] who claimed that increased innovation is a significant capturing indicator in financial institutions. Financial innovation thus shapes bank investment decisions and helps to create banks' success. However, Grinblatt et al. [27] document that when performance exists, individual investors earn abnormally. To prevent complexities, innovation is employed to quantify risk in multiple ways. Thus, investor behavior is influenced by market performance. Also, institutional investors employed innovation technologies to select protection from market efficiencies and feel secure in their investment decisions. Naidoo & Hoque [105] claimed that investors do not have adequate skills and knowledge for investments. They, therefore, use innovations in the decision to invest. Ogunlusi & Obademi [11] document that finance theory offers tools to understand markets' fluctuations and development as well as the rationality of market beliefs. That is, this analysis considers current market attitudes, allowing us to assess how the banking industry responds to innovation for investment decisions. Our findings are further partially consistent with Wu & Pea-Assounga [16], who showed that fintech is related positively and significantly to sustainability and competitive advantage, which in turn straightened the firms' rational investment choices.
In conclusion, we developed a theoretical framework based on RBVT. This study gathered data from Congolese banks to examine the “moderating effect” of internet security on the link between financial innovation and investment decisions. The findings demonstrate that financial innovation is positively and statistically linked to stakeholders' satisfaction, and bank performance, which ultimately influence ID. All of the research's mediator variables partially mediated the effect of FI on ID. Also, internet security directly affects ID but does not moderate FI-ID. This study employs Structural Equation Modelling to assist banks in understanding when and how to optimize the effects of their workers' innovative behaviors while reducing the negative effects.
6. Research implications
Against the backdrop of burgeoning discussions surrounding financial innovation, stakeholders' satisfaction, bank performance, and internet security in the context of banks' investment decision-making, this research not only unveils significant determinants but also holds profound theoretical implications, shedding light on the dynamics within the Resource-Based View (RBV) framework. This study advances the theoretical understanding of the RBV by delineating how improvements in internet security, coupled with enhanced financial innovation and customer satisfaction, can lead to increased customer loyalty, trust, and, consequently, informed investment decisions. For instance, findings indicate that internet security has a positive and significant effect on investment decisions. Furthermore, internet security is significantly and positively correlated with financial innovation, this implies that customers and financial institutions can trust and feel safe when doing online transactions. Various studies have suggested that internet security is a significant factor, that obstructs customers from adopting or using financial innovation services [5,81,87]. This research, however, reveals that if internet security is advanced, privacy and security issues have less negative effects on customers' intention to use online services and transactions. This research offers a better understanding of CS, ES, and BP in investment decision-making and their mediating roles, as the findings indicate that financial innovation has direct and negative effects on investment decisions, whilst, with greater internet security, it has significant and effective impacts. Finally, this research emphasizes the reasons why internet security must be considered and how it may be improved. Lastly, managers should keep in mind that various aspects of innovation have diverse long-term impacts on investment decisions. As a result, they must spend more resources on distinctive components depending on the operative investment stressed by their market strategies. If their organizations need to increase the rewards of investment, they should put more effort into enhancing stakeholders' satisfaction. If, on the other hand, the plan's top aim is to grow income from investments, customers' happiness must be given special consideration, and strategies to improve performance. Such consequences are particularly relevant for HRM in the banking sector. Their employees may have superior expertise, skills, and abilities that apply to their positions. The development of human resources is not a primary issue in this context.
In essence, the RBV lens allows for an exploration of how the firm's internal resources, capabilities, and strategic orientations shape its investment decisions in the dynamic landscape of financial services. The findings underscore the theoretical importance of internet security as a catalyst for positive investment decisions. Contrary to some existing literature highlighting internet security as a hindrance to financial innovation adoption, this study, framed within RBV, argues that robust internet security fosters trust and safety, minimizing the negative impact of security concerns on customers' intention to engage in online transactions. Within the RBV framework, the research delves into the mediating roles of Customer Satisfaction (CS), Employee Satisfaction (ES), and Bank Performance (BP) in the context of investment decision-making. Understanding how these internal factors mediate the direct negative effects of financial innovation on investment decisions becomes pivotal. For instance, the study suggests that with heightened internet security, the mediating effects of CS, ES, and BP become more significant and effective. The theoretical implications extend to strategic resource allocation within organizations. The research posits that various components of innovation have diverse long-term impacts on investment decisions. Consequently, managers, guided by RBV, are advised to allocate resources strategically based on their market strategies and operational investment priorities. This aligns with the RBV principle that organizations must leverage unique, valuable, and non-substitutable resources to achieve sustained competitive advantage. The study emphasizes the relevance of RBV to Human Resource Management (HRM) in the banking sector. Acknowledging that employees in the sector possess unique expertise and skills, the RBV lens emphasizes that the development of human resources is not a one-size-fits-all matter. Depending on the organizational goals, whether increasing investment rewards or enhancing income, HR strategies should align with the distinctive components highlighted by RBV.
7. Limitations and future research possibilities
This research is not free from limits, which emphasizes the road for future studies. There may be other precursors and contributing variables from a broader viewpoint. The presence of only one moderator factor (i.e. internet security) may, therefore, be viewed as a limitation. Also, the suggested research model was examined in a single developing country through a survey of banks’ employees and customers. The results reflect a snapshot of a specific period; however, the effects of internet security are unpredictable over the period. Therefore, future studies may use the second data and include other countries once the data are available. The current study has not linked internet security with customer satisfaction, and bank performance, and also did not link privacy and perceived risk with trust. Thus, it may be interesting to examine internet security and customer satisfaction or bank performance related to financial innovation, as well as the moderating role of perceived risk, gender, and age. The perceived security and privacy issues are likely to be affected by the country's economic aspect, uncertainty, and technological advances that raise the potential need for cross-national investigations. Therefore, future studies may add to this model other variables related to economic activities such as interest rate, gross domestic product (GDP), and inflation. Moreover, investigations may also assess services based on web and internet security separately. Ultimately, the moderating effects of internet security should be conducted in both developing and developed countries.
Funding
“This work is supported by Jiangsu Excellent Postdoctoral Talent Funding (2022ZB647).”
Availability of data and materials
The data that support the findings of this study are available from the corresponding author upon reasonable request.
CRediT authorship contribution statement
Jean Baptiste Bernard Pea-Assounga: Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Data curation, Conceptualization. Hongxing Yao: Validation, Supervision, Project administration, Funding acquisition, Conceptualization. Grace Mulindwa Bahizire: Writing – original draft, Visualization, Validation, Formal analysis. Prince Dorian Rivel Bambi: Writing – review & editing, Validation, Formal analysis, Data curation. Jonathan Dior Nima Ngapey: Writing – review & editing, Validation, Investigation.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e27242.
Contributor Information
Jean Baptiste Bernard Pea-Assounga, Email: aspeajeanbaptiste@yahoo.fr, aspeajeanbaptiste@ujs.edu.cn.
Hongxing Yao, Email: hxyao@ujs.edu.cn.
Grace Mulindwa Bahizire, Email: gbahizire@gmail.com.
Prince Dorian Rivel Bambi, Email: princebambi315@gmail.com.
Jonathan Dior Nima Ngapey, Email: jonathandiornima@gmail.com.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
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Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.