An Empirical Study of Digital Lending in India and the Variables Associated with its Adoption

ABSTRACT

Objective:

the digital lending platform is a significant and innovative business concept in the field of financial technology. It creates a direct connection between lenders and borrowers. Scientists studied the technique by which digital lending companies use leverage. This study proposes an updated technology acceptance model (TAM) to investigate the factors influencing consumer adoption of digital lending platforms. More specifically, it examines how service quality and perceived threat influence trust development.

Methods:

this study employs structural equation modeling (SEM) to investigate the potential links between the underlying variables.

Results:

the study found that customers’ attitudes toward adoption of digital lending platforms are highly influenced by trust, perceived value, and perceived ease of use. The quality of service has a considerable impact on consumers’ perceptions of risk while using digital lending services. The generated model corresponds to the findings of previous studies.

Conclusions:

the findings of the continuing research are important for optimizing platform marketing strategies and translating strategic goals into concrete activities. To improve future research, we recommend integrating more variables to have a more comprehensive understanding of the adoption intentions of digital lending platforms.

Keywords:
digital lending; adoption intention; technology acceptance model; structural equation modeling; fintech

INTRODUCTION

The significant influence of technology on the provision of financial services has resulted in substantial and innovative transformations in the industry. Indeed, ‘fintech’ is a fusion of ‘finance’ and ‘technology’ that has the potential to completely transform financial management. In the last 10 years, innovative technology has completely transformed both financial markets and society. The integration of technology in the financial sector is not a recent occurrence, but there has been a notable change in the past decade. Another contributing element is the significant diversity within the financial technology industry, particularly in terms of the size of companies involved.

Technology companies are endeavoring to enhance the banking experiences of consumers. Prominent banks and other financial institutions are making substantial investments in state-of-the-art technology to enhance their fundamental operations. Fintech services facilitate distinctive business-to-consumer, peer-to-peer, and business-to-business transactions (Schierz et al., 2010).

Fintech lenders challenge traditional banks by integrating financial services with advanced technologies. These lenders integrate finance and technology. Traditional banks are experiencing decreased customer loyalty as consumers become more inclined toward digital alternatives. Furthermore, there is a growing level of digital literacy among consumers, leading to changes in their expectations about service quality, security, and effectiveness. These reasons are contributing to the increasing popularity of these institutions. Real-time money management through advanced financial services is a crucial aspect of fintech’s future. Fintech lenders, who function autonomously from traditional banks, provide consumer loans using distant digital information technology platforms. In the last 10 years, fintech companies in India have undergone substantial growth by focusing on specific customer segments. To accommodate this expansion, there has been a rapid development of regulatory structures. For example, there has been a 40% decline in manual KYC (know your customer) procedures in favor of digital KYC, an 85% cost reduction through the use of e-KYC, and several other enhancements.

India had a compound annual growth rate (CAGR) of 39.5% over a 10-year period as a result of the emergence of digital lending firms. According to a survey conducted by Experian, the digital lending industry in India was valued at USD 270 billion in 2022 and is expected to grow to USD 350 billion by 2023. Furthermore, the Indian loan market experienced a notable 11% rise in disbursement, reaching Rs 174 trillion in FY22, as opposed to Rs 11.4 trillion in FY17. The compound annual growth rate (CAGR) of 72% is quite impressive. According to a report by Praxis Global Alliance (2023), the disbursement is projected to increase and reach Rs 274 trillion in FY26, with a compound annual growth rate (CAGR) of 12%.

India is a developing nation; therefore, a large majority of its population hails from middle-class or low-income households. Several studies have utilized the technology acceptance model (TAM) to elucidate consumer behavior and their inclination to adopt technological improvements. The primary constituents of the TAM model include perceived utility, perceived ease of use, attitude, trust, and adoption intention. Moreover, other investigations have been conducted to evaluate how demographic variables affect user attitudes toward the use of technology. India’s economic progress has led to a significant portion of its population belonging to the middle-class or low-income segments. A fundamental issue occurs when depending on banks and other financial instruments to get loans and access the loan funds. Traditional banks have experienced a decline in client visits as a result of the COVID-19 outbreak and the necessity to adhere to social distancing protocols. The majority of individuals in India come from impoverished or middle-class backgrounds, as it is a developing country.

There is an issue regarding the processing of loan applications and the distribution of loans through banks and other financial instruments. An article in a trade magazine highlights the increased demand for online lending platforms because of reduced customer visits to traditional financial institutions as a result of the COVID-19 pandemic and social distancing measures (DQINDIAOnline, 2021). The 2020 India fintech research by MEDICI utilizes advancements in digital technology such as Aadhar verification, eKYC, and digital payment methods like UPI and Google Pay to obtain credit scores and expedite loan disbursements, regardless of whether the borrower is from an urban or rural area. Digital lending facilitates the provision of credit to low-income individuals through peer-to-peer lending organizations that have a social objective (Nguyen et al., 2022). Utilizing cost-effective technology, digital lending addresses the limitations of traditional offline lending.

Digital lending services represent a pioneering effort within the banking sector to incorporate digital technology. Prior research in this domain mostly concentrates on the substance and arrangements of the services, whereas the usage patterns of digital lending platforms have received limited investigation. The understudied nature of this topic and Indian customer decision-making is evident. In addition to addressing the issues mentioned, the study aims to outline the criteria Indian clients evaluate when choosing fintech digital lending services. The authors Zhao et al. (2017)conducted a thorough investigation into the realm of digital lending, with a particular emphasis on the digital lending platforms that are the most well-known on a worldwide scale. After thoroughly comparing these systems’ operational routines, they found a successful classification method. This domain has many unanswered questions, including pricing, method improvement, risk mitigation, privacy protection, and customization. However, during the previous six years, there has been little research on user uptake of digital lending, particularly in India. Two studies examine SMEs, while the third examines people.

This study specifically examined the usage patterns of digital lending platforms in light of the given situation in India. This research fills a gap in the literature on why Indian people join digital lending services. Along with the marketing strategy, it is crucial to grasp the user’s behavioral objectives and their influencing elements (Chulawate & Kiattisin, 2023). This is essential for effective and efficient platform design and construction. Using the technology acceptance model (TAM), this study investigated how many factors influence people’s willingness to embrace new technology. We used the TPB, UTAUT, and TAM models to analyze this phenomenon (Rahman et al., 2017). In the technology acceptance study, TAM outperformed other models. To suit this research, we can adapt the technology acceptance model (TAM) or add factors such as service quality, perceived risk, and trust. This study compares digital lending to traditional lending and investigates how it stimulates innovation in the firms that receive it.

The article arranges the remaining sections in the following manner: the literature review is a succinct summary of the literature that includes the conceptual framework, hypotheses, and assumptions. The research methodology focuses on the design of research instruments and the collection of data. The results offer a succinct summary of the findings.

LITERATURE REVIEW

Digital lending

The process of digitalization has led to significant economic changes, which are referred to as the present industrial age. These organizations are considered a component of Industry 4.0 since they enable all economic entities to collaborate and generate value through digitization (Adamek & Solarz, 2023). The financial services business is undergoing transformation as a result of the increasing prevalence of information and communication technology. According to the source, the convergence of technology and financial resources has led to the emergence of ‘fintech.’ According to Kaji (2021), the word was initially used in the 1980s, but it first acquired prominence during the financial crisis that occurred in 2007-2008, which coincided with the growth of fintech.

According to the definition provided by the Bank for International Settlements (2018), fintech is a type of financial innovation that is technology-driven and results in the creation of new business models, applications, procedures, or products. They have a substantial influence on the institutions, services, and markets of the financial sector (Nguyen & Dang, 2022). The term ‘financial technology lending’ or ‘credit’ is also used by other sources (Berg et al., 2022; Cornelli et al., 2023). The term ‘fintech lenders’ is defined by Agarwal and Chua (2020) as businesses that provide loans remotely, without needing applicants to interact with employees or visit the lender.

According to Berg et al. (2022), fintech lending encompasses two main aspects: direct interaction between customers and lenders, and the utilization of technology to evaluate and monitor borrowers. Thanks to technological advancements, many traditional financial institutions are now able to process loan applications through online platforms and evaluate applicants using unconventional data. A financial technology lender is a non-bank lending institution that works independently from traditional banks and does not engage in deposit-taking activities, as determined by several research investigations.

Fintech, often called financial technology, refers to non-bank lending institutions that use digital information technologies to simplify the loan application process for customers (Dorfleitner et al., 2017).

Research investigating the application of new information technologies in the financial sector, specifically in the realm of financial technology (fintech), employ various theories, models, and conceptual frameworks. Their theoretical framework facilitates the discovery and assessment of the variables and factors that impact intents and/or foster the advancement of innovative technology.

The current investigation on the adoption of fintech utilizes the technology acceptability model (TAM) to identify and evaluate the elements that impact the acceptability of fintech services. Several studies, including Hu et al. (2019), Balcázar and Rivas (2021), Putranto and Sobari (2021), Nugraha et al. (2022), Ajzen and Fishbein (1977), Hubert et al. (2018), Kumar et al. (2020), Kurniawan (2019), and Rosavina et al. (2019), provide evidence that supports this assertion. This approach forecasts and explains the adoption of new technologies, which contributes to their widespread use and advantageous outcomes (Nugraha et al., 2022). As a result of its adaptability, we are able to incorporate other components into the research, which assists us in comprehending the factors that motivate the use of contemporary information technology (Bagozzi et al., 1989; Zhao et al., 2017).

Formulation of hypotheses and the proposed conceptual framework

Davis proposed the technology acceptance model (TAM) to elucidate the effects of many elements on consumer behaviors and intentions. TAM is built upon the theoretical framework of the theory of reasoned action (TRA) model.

Figure 1 illustrates the technology acceptance model (TAM), which proposes that people’s intentions to adopt a technology may be explained by their perception of its usefulness and ease of use. Perceived usefulness (PU) refers to the extent to which consumers believe that their job performance will be enhanced by using a specific technology. Perceived ease of use (PE) is a metric that gauges individuals’ perception of the system’s user-friendliness and simplicity.

Figure 1
Technology adoption model.

The technology acceptance model (TAM) has received sustained accolades from experts since its initial publication. It has been widely utilized in research on the adoption of technology throughout the years. The technology acceptance model (TAM) is a versatile framework that can be modified or extended in various ways. Consequently, there have been multiple developments in the field of fintech, such as research on mobile digital lending applications (Putri et al., 2023), fintech services for banking customers, and the relationship between fintech and the banking industry (Lema, 2017).

Perceived usefulness

Perceived usefulness, in the technology acceptability model (TAM), is also the most important component in user acceptability (Davis, 1989). The perceived utility of the system is correlated with its productivity, efficacy, and overall advantages in enhancing user performance. The fundamental principles of the TAM state that an individual’s utilization of technology is influenced by their acceptance of that technology, which is in turn driven by two cognitive factors: perceived usefulness (PU) and perceived ease of use (PEOU) (Brandon-Jones & Kauppi, 2018). Put simply, it refers to the degree to which an individual believes that utilizing a technology would enhance their work performance. Consequently, the level of consumers’ inclination to utilize a technology increases in direct proportion to its level of usefulness (Yang & Lee, 2016).

H1: Perceived usefulness (PU) has a positive effect on adoption intention (AI).

Perceived ease of use

Perceived ease refers to the extent to which accessing a technology system and its presentation is considered effortless (Venkatesh, 2000). According to Davis (1989), the technology acceptance model (TAM) suggests that users’ perception of how easy a system is to use is a crucial component in their acceptance of it. In his study, Davis (1989) provided a definition of ease of use as the degree to which users perceive that a particular system will allow them to perform tasks without exerting significant effort. Put simply, the greater the perceived user-friendliness of a system, the stronger the user’s inclination to utilize the system. The fundamental principles of the TAM argue that an individual’s utilization of technology is influenced by their willingness to embrace that technology, which is in turn influenced by two cognitive factors: perceived usefulness (PU) and perceived ease of use (PEOU) (Brandon-Jones & Kauppi, 2018). TAM aimed to discover the essential variables proposed by earlier research. The study by Teo et al. (2011) examines the connections between perceived utility, perceived ease of use, attitude toward computer use, and intention to utilize technology.

H2: PEU has a significant impact on AI.

H3: PEU has a significant impact on PU.

Trust

Trust (TR) is a complex and diverse notion that is highly relevant in the context of economic transactions (Xie et al., 2021). TR has consistently prioritized adoption and is often employed as a supplementary basis for consumer attraction, alongside PU (Jin et al., 2014) and PE. The significance of TR’s function is heightened in scenarios involving financial technology applications as a result of the service’s substantial and high-dimensional data. Prior research has identified customer service and satisfaction, perceived risk, brand image, information quality, government support, and service quality as variables that influence TR in fintech innovation adoption (Chopdar et al., 2018; Singh & Sinha, 2020). Consequently, it is critical to investigate how TR influences the attitudes and adoption propensity of prospective users. Consequently, the subsequent hypothesis was formulated:

H4: TR has a significant impact on AI.

Quality of service (QoS)

Quality of service (QoS) is a client’s assessment of the service provider’s performance compared to their expectations (Parasuraman et al., 1988). A firm’s effectiveness and financial success depend on service quality. In the field of service providing, a service is a unique form of product that is distinguished by its intangibility (Hai et al., 2017). A high-quality score (QoS) can give a company two market advantages. It makes it easier to attract new clients. It also retains consumers by providing high-quality goods and services, which makes them happier and motivates them to buy again. Using this strategy, businesses may save costs, boost efficiency, and increase profits. The quality of service (QoS) measures how a service affects corporate performance, adoption intentions, and customer happiness. According to previous studies, quality of service (QoS) significantly affects perceived risk (PR) (Sweeney et al., 1999) and trust (TR) (Kalia et al., 2021). Based on this, the following hypotheses were formulated:

H5: QoS has a significant impact on TR.

H6: QoS has a significant impact on PR.

Perceived risk (PR)

Perceived risk (PR) is the anticipation of likely undesirable outcomes (Raza et al., 2017). Meanwhile, Ko et al. (2004) defined perceived risk as consumers’ views of the many and frequently conflicting consequences of buying a service or product. Consumer behavior may be understood using public relations theory (Armitage & Conner, 2010). Most scholars believe perceived risk (PR) is the most critical element in technological acceptance. However, perceived risk is TR, or technology readiness. This article defines public relations as fintech customers’ perceived privacy risk. This risk includes transaction data exposure, personal data breaches, and other personal information disclosure. Previous study has shown that risk perception affects cloud, mobile banking, and fintech adoption (Ryu & Ko, 2020). Consequently, the ensuing hypothesis was developed on the basis of the study that had been done previously.

H7: PR has a significant impact on TR.

CONCEPTUAL FRAMEWORK

This research employs the technology acceptance model (TAM), trust and risk (T), perceived quality of service (QoS), and perceived risk (PR) to assess the intention of individuals to embrace the digital lending platform, based on prior research and theoretical concepts. The behavioral adoption intention (AI) is the dependent variable, while the independent variables are PU (perceived usefulness), PE (perceived ease of use), T (trust), QoS (perceived quality of service), and PR (perceived risk). Figure 2 displays a visual depiction of the proposed idea. The combination encompasses a comprehensive array of elements: PU, PE, T, QoS, PR, and AI, which can enhance the accuracy of predicting user behavior in relation to their inclination to join the digital lending platform. The assumptions evaluated in this study are shown in Table 1.

Table 1
Hypotheses in the study.

Research hypotheses	Hypothesized path	Sources
H1	PU → AI	Armitage and Conner (2010), Nguyen et al. (2022), Ryu (2018), Ajzen and Fishbein (1977), Adamek and Solarz (2023), Balcázar and Rivas (2021)
H2	PEU → AI	Armitage and Conner (2010),Nguyen et al. (2022), Ryu (2018), Ryu (2018), Ajzen and Fishbein (1977), Adamek and Solarz (2023), Balcázar and Rivas (2021), Kumar et al. (2020)
H3	PE → PEU	Ryu (2018), Ajzen and Fishbein (1977), Xie et al. (2021)
H4	T → AI	Nguyen et al. (2022), Ryu and Ko (2020), Kumar et al. (2020)
H5	QS → T	Ryu (2018), Ajzen and Fishbein (1977), Adamek and Solarz (2023), Balcázar and Rivas (2021), Kumar et al. (2020)
H6	QS → PR	Ryu and Ko (2020), Ryu (2018), Ajzen and Fishbein (1977), Adamek and Solarz (2023), Balcázar and Rivas (2021),
H7	PR → T	Nguyen et al. (2022), Ryu (2018), Ajzen and Fishbein (1977), Adamek and Solarz (2023), Balcázar and Rivas (2021), Kumar et al. (2020)

• Note. Developed by the authors.

Figure 2
Proposed hypothesis model.

Upon analyzing the data, it becomes evident that there are notable disparities between genders when it comes to online consumer credit. Women make up 41.7% of the total users. These findings align with the research conducted by Trafimow et al. (2004), indicating that women tend to be less involved in credit activities. This could be attributed to the higher consumption demands of women compared to men in the consumer market, as suggested by Cornelli et al. (2023). Regarding the occupation of participants, the majority of them are students, making up 36.2% of the total. The age distribution of respondents skews heavily toward young individuals between the ages of 18 and 26, making up a significant majority at 95.02%. These findings align with the perspective of Berg et al. (2022), who assert that age correlates with the utilization of online lending platforms, as younger individuals tend to be more inclined toward their use. When it comes to income sources, it’s interesting to note that a significant number of respondents, a total of 119 individuals or 21.2%, rely on the financial support provided by their families to cover their living expenses. Additionally, over half of them (44.07%) have encountered the situation of using loans to back other loans. The data reinforces the significance and immediacy of our research. To effectively address the issue of young people’s online consumer credit, it is crucial to have a deep understanding of the key factors that influence their behavior. This knowledge will enable us to develop targeted interventions and promote responsible usage of digital lending products.

RESEARCH METHODOLOGY

Research model

This paper constructed a research model that was derived from the UTAUT model, as indicated by the aforementioned analysis. The theories of perceived risk, trust, and perceived advantage were used to select five constructs: perceived utility, perceived ease of use, perceived risk, quality of service, trust perceived advantage, adoption intention, and use behavior. The model was employed to investigate the behavior of utilizing contactless financial services (Figure 2).

Data collection

The data was gathered using an online survey. We conducted a research study in the states of Andhra Pradesh and Telangana, which collectively represent around 20% of fintech utilization in India. The survey was conducted from November to December 2023. A total of 590 participants actively participated in the survey by successfully completing the accessible Google Forms. The study utilized a random sample technique to choose the individuals who took part in the investigation. These individuals are prior users of digital lending services. The questionnaire began with a brief introduction to the digital lending platform and a detailed explanation of the research’s goals. The research involved several occurrences of authorized digital lending services. The examples covered a wide range of lending methods, including peer-to-peer (P2P) lending, collateral lending, buy now, pay later (BNPL) loans, point-of-sale (PoS) financing, crowdsourcing, and online and mobile lending platforms. The questionnaire comprises inquiries pertaining to various adoption attributes, such as perceived utility, perceived simplicity, trust, service quality, and perceived danger.

We eliminated surveys that did not fulfill the criteria because we were concerned about the time it took to respond and the non-random nature of their completion. The astonishing aspect of our situation is that we have received a grand total of 561 responses, which is especially impressive since our response rate was an extraordinarily high 95%. The descriptive values presented in Table 2 were obtained from surveys that evaluated the demographic information of individuals. Factors such as age, gender, educational attainment, employment situation, income level, and utilization of digital lending services are taken into account. The age distribution is predominantly defined by the age cohort of individuals between 18 and 24 years old, which constitutes the majority with a share of 54.5%. These individuals continually adopt and adapt to new lifestyles and technology advancements. This sampling is considered appropriate. The widespread appeal of digital lending services is evident from the high acceptance rate among over 75% of responders and technology users. The study modifies and customizes the questionnaire according to the specific attributes of the peer-to-peer lending platform under investigation, taking into account relevant previous research (Sunardi et al., 2021). The TR approach was developed based on the experiments conducted by Dias et al. (2022), as well as those conducted by Lien et al. (2015). The research conducted by Lien et al. (2015) and Johnson et al. (2018) formed the basis for the creation of the PE technique. The PU technique was developed based on a study conducted by Nugraha et al. (2022). Ryu (2018) conducted a study that led to the establishment of the QS method in response to their findings. The public relations approach was designed based on the findings obtained by Johnson et al. (2018). The scale was employed to evaluate the impact of each of the six external influences, with each item being assessed using a range of three to four distinct variables. The assessment of the 19 items was conducted using a Likert scale consisting of five points: strongly agree, agree, disagree, neutral, and strongly disagree.

Table 2
Descriptive statistics.

Demographic variable	Category	Frequency	Percentage
Age	18-24	306	54.5
	25-34	151	26.9
	35-54	75	13.4
	54 and above	29	5.2
Gender	Male	327	58.3
	Female	234	41.7
Education	Doctorate	24	4.3
	Postgraduate	214	38.1
	Graduate	316	56.3
	High school graduate	7	1.2
Occupation	Business	86	15.3
	Employed	232	41.4
	Self employed	40	7.1
	Student	203	36.2
Income	0	119	21.2
	0-10,000	50	8.9
	11,000-20,000	51	9.1
	21,000-35,000	156	27.8
	35,000-50,000	152	27.1
	50,000 and above	33	5.9
Digital lending platform usage	Yes	422	75.2
	No	139	24.8

• Note. Developed by the authors.

RESULTS AND DISCUSSION

To evaluate the data analysis, we need to implement a two-stage analytical technique. We first validate the accuracy of the measurement model and then evaluate the validity of the provided hypotheses. As a result, the first part of the analysis consisted of monitoring the method in which the items were loaded onto the model constructs, which is also referred to as the measurement model. The third phase consisted of putting assumptions to the test by analyzing the connections between the various model constructions, which is something that is referred to as the structural model. The study’s findings indicate that the software Smart PLS 4.0 assisted in the analysis of the measurement and structural models.

We conducted convergent validity tests using composite reliability (CR) and average variance extracted (AVE) measures to assess the accuracy of the components within the study model. Every single value associated with the AVE assessment exceeded the threshold of 0.5, and every single value associated with the CR assessment exceeded the prerequisite of 0.70. The outcome validated the convergent validity. Table 3 provides a presentation of the findings. We did not use Cronbach alpha (CA) as a measure of validity in this particular investigation because its ‘lower bound value’ understates the true dependability of the instrument. To put it another way, this indicator operates on the assumption that each and every item on the build has an equivalent loading. This leads to a risk of underestimating the dependability of its internal consistency, which is influenced by the number of items included in each construct (Peterson & Kim, 2013). In addition, using partial least squares (PLS), some items are more relevant for a certain construct than others. This creates unique outer loadings for that construct. These irregularities are already considered when determining the dependability of composites.

Table 3
Measurement instruments.

Variables	Items	Measurement	Sources
PU	PU1	Customers’ needs can be fulfilled by digital lending platforms	Balcázar and Rivas (2021)
	PU2	Customers save a lot of time when they use digital lending services	Balcázar and Rivas (2021)
	PU3	Customers using digital lending platforms can access several facilities	Balcázar and Rivas (2021)
	PU4	I find the digital lending systems to be helpful overall	Balcázar and Rivas (2021)
PEU	PEU1	Customers may operate the digital lending apps with ease	Xie et al. (2021)
	PEU2	It is clear and easy to understand how to use the digital lending apps	Xie et al. (2021)
	PEU3	Utilizing an online lending platform is effortless	Xie et al. (2021)
T	T1	Digital lending has robust data security	Zhao et al. (2017)
	T2	Customers utilize the digital lending platform with trust	Zhao et al. (2017)
	T3	I trust digital lending service apps and transactions done by digital lending	Zhao et al. (2017)
QoS	QoS1	The digital lending platform promptly addresses my requirements	Adamek and Solarz (2023)
	QoS2	The digital lending platform have the expertise to address my inquiries	Adamek and Solarz (2023)
	QoS3	The digital lending platform understands and meets my specific needs	Adamek and Solarz (2023)
PR	PR1	I have some concerns to share personal information through digital lending platforms	Kumar et al. (2020)
	PR2	I am concerned about unauthorized access to my account on the digital lending platform	Kumar et al. (2020)
	PR3	The financial risk associated with utilizing a digital lending platform would be greater in comparison to conventional lending methods	Kumar et al. (2020)
AI	AI1	There is still ample potential for the growth and expansion of digital lending apps	Balcázar and Rivas (2021)
	AI2	People are more likely to tell their family or friends about digital lending platforms	Balcázar and Rivas (2021)
	AI3	In the future, I intend to utilize the digital lending platform	Balcázar and Rivas (2021)

• Note. Developed by the authors.

We tested the discriminant validity component of the research model by taking the square root of the extracted average variance (AVE) to further analyze the construct validity. We conducted this assessment to enhance the validity of the construct. The results shown in Table 4 reveal that the square root of the average variance extracted (AVE) for each construct is higher than the correlation with the other constructs. This factor demonstrates the existence of discriminant validity.

Table 4
Reliability and validity measures.

Variables	Items	Factor1	Factor2	Cronbach’s alpha	AVE	CR
PU	PU1	.688		0.730	0.650	0.747
	PU2	.695
	PU3	.603
	PU4	.768
PEU	PEU1	.811		0.767	0.683	0.772
	PEU2	.732
	PEU3	.716
T	T1	.759		0.787	0.699	0.808
	T2	.828
	T3	.799
QoS	QoS1	.704		0.800	0.625	0.807
	QoS2	.776
	QoS3	.523
PR	PR1		.810	0.754	0.671	0.824
	PR2		.820
	PR3		.758
AI	AI1	.647		0.835	0.753	0.841
	AI2	.754
	AI3	.633

• Note. Developed by the authors.

According to Berger and Sellke’s (1987) findings, a t-value greater than 1.96 indicates statistical significance at a level of confidence less than 0.05. A t-value greater than 2.58 indicates statistical significance at a level of confidence lower than 0.01. If the t-value is greater than 3.29, it is considered statistically significant at a confidence level of less than 0.001.

As seen in Figure 3, a t-value of 41.644, which is statistically significant, suggests that there is a strong association between the two variables: perceived utility and ease of use. The statistical significance of the t-value confirms this. There is a positive correlation between customers’ judgments of the simplicity of digital lending platforms and their perceptions of the convenience of using those services. The findings are consistent with conclusions drawn from previous research on the use of online lending services.

Figure 3
Structural equation model.

As shown in Table 5, there was a strong relationship between trust and adoption intention, as demonstrated by the findings of the H4 test (t = 4.613). To be more specific, the descriptive analysis indicates that the dependent variable, T, has an average value of 5.71, which is the highest value. Customers have a tremendous deal of faith in the digital lending platform because they believe it will provide a satisfactory kind of service. Users are more likely to trust these services by virtue of the fact that certain digital lending platforms have registered with the regulatory authority. It is consistent with the findings of previous investigations (Xie et al., 2019).

Table 5
Results of the PLS analysis (values generated by SmartPLS 4.0).

	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	T statistics (|O/STDEV|)	P-values
PEU → AI	-0.026	-0.016	0.219	0.119	0.906
PR → Trust	-0.053	-0.054	0.028	1.910	0.059
PU → AI	0.173	0.171	0.208	0.834	0.406
PU → PEU	1.045	1.047	0.025	41.644	0.000
QoS → PR	-0.214	-0.218	0.061	3.533	0.001
QoS → Trust	0.942	0.940	0.024	40.018	0.000
Trust → AI	0.806	0.796	0.175	4.613	0.000

• Note. Developed by the authors.

An extremely strong correlation exists between trust (T) and service quality (QS), as demonstrated by the findings (H5: t = 40.018). Considering this result, we can conclude that a digital lending platform can build client confidence and increase adoption if it provides exceptional service. Previous research has observed a similar pattern of findings.

The correlation between quality of service and public relations (H6: t = 3.533) significantly influences customers’ perceptions of risk when using digital lending platforms. Consumers are more likely to embrace a digital lending platform after experiencing excellent service from that platform, according to this research, which provides more evidence to support this hypothesis.

The research revealed a significant degree of connection between PR and TR (H6: t = 4.613). This research demonstrates how customers’ perceptions of risk significantly influence their level of confidence in digital lending services. Previous research that investigated how consumers felt about purchasing online have shown that these findings are consistent with theirs.

CONCLUSION

The use of online lending services has grown substantially in recent years. Technology, functional efficiency, usability, and accessibility are all contributing elements that are speeding up this expansion. While India’s traditional banks remain the backbone of the country’s banking and financial sector, digital lending has many advantages for businesses.

The ease of getting a loan sanctioned digitally has made digital lending flourish in India, in contrast to the traditional lending mechanism’s cumbersome loan application process. Digital lending in India had a breakout year in 2022, for a number of reasons, according to an interview with Indifi Technologies CEO Alok Mittal conducted by Rudra (2022). One is the resiliency and agility of the Indian market, which has helped businesses recover. Along with confidence in digital services, technological advancements and digital capabilities play a role. All of these things have come together to make digital lending a practical choice for businesses (Rudra, 2022). In his speech, Mr. Mittal emphasized the role of the Reserve Bank of India (RBI) in establishing rules for online lenders to follow and in preventing a repeat of China’s disastrous trend of high default rates among lending institutions. Digital lending has made strides forward with the Data Protection Bill, the Account Aggregator framework, and the RBI guidelines for loss-sharing arrangements (Rudra, 2022). To ensure effective supervision and monitoring across the fintech industry for e-lending platforms, digital banks, and neobanks, the central regulator, the Reserve Bank of India (RBI), plays an integral role (Arun et al, 2023). The Indian digital lending market now has over 100 companies or institutions lending USD 270 billion. Digital lending encompasses various economic sectors, including consumer loans, SME/MSME loans, invoice discounting, and financial inclusion platforms like Jai Kisan and Avanti (The Digital Fifth, 2023).

Financial innovation affects recipient firms in many ways. One benefit is increased efficiency and effectiveness in bank operations, leading to cost reduction and increased profitability. It can also increase risk, especially when new financial products or services are poorly understood or regulated. Financial innovation can impact the stability and integrity of the financial system, as seen in the 2008 financial crisis, which was partly caused by the proliferation of complex financial instruments.

Banking’s financial innovation enhances the availability of financial services. Mobile banking provides access to financial services that were previously inaccessible to millions of people. Digital payments have enhanced the speed and convenience of transactions, hence diminishing the necessity for cash and checks. According to McKinsey, the digital revolution has had a significant impact on retail banking. Revenues from online banking have been growing at a rate of 6% per year, while digital sales have been expanding by 10% yearly between 2014 and 2021 (Bhattacharyya et al., 2023). A PwC survey found that mobile banking usage has increased to 46% globally, with 82% of users accessing their accounts weekly. A survey found that digital banking increased customer satisfaction by 13% and reduced churn by 15% (PricewaterhouseCoopers, 2021). The adoption of fintech and digital solutions has helped banks enhance operational efficiency and cut costs. For instance, RPA and AI enable banks to automate routine processes, saving up to 80% in costs (PricewaterhouseCoopers, 2021). The majority of participants in the financial services sector identify fraud detection as the main use for artificial intelligence. Artificial intelligence improves fraud detection by combining supervised and unsupervised learning algorithms to gain a deeper understanding of client behavior. Gaining a more profound comprehension of client behavior enables organizations to identify and thwart unauthorized activities.

The McKinsey analysis indicates that there was a 10% rise in global fintech investment, reaching a total of $105 billion in 2020. The COVID-19 epidemic expedited the adoption of digital technology in financial services by three to four years, leading to a 5% rise in digital customer engagements and a 20% surge in new digital consumers. Financial innovation has led to the emergence of new banking products and services. Mobile banking has experienced significant exponential growth in recent years. The results of a poll conducted by the Federal Reserve indicate that the percentage of adults in the United States utilizing mobile banking increased from 43% in 2015 to 63% in 2022 (Banking Technology Vision, 2023). New peer-to-peer (P2P) lending platforms have disrupted the traditional lending market (Sunardi et al., 2022). P2P lending platforms in the U.S. generated over $22 billion in loans in 2022 (GSMA, 2023).

Forecasts indicate that the digital lending industry will experience growth as a result of the implementation of digital banking. The banking industry’s digital innovation is revolutionizing mobile money, peer-to-peer (P2P) transactions, and marketplace finance. According to a research conducted in January 2022 by European Banking Supervision, a regulatory agency responsible for maintaining financial stability in the banking system of the European Union, there has been a 23% increase in the number of digital users since the start of the epidemic. Consequently, the growth of P2P lending can be attributed to the digitalization of the financial industry (The Business Research Company, 2023)

Traditional banks face competition from non-bank financial intermediaries such as fintech companies, which has negative implications. Financial services provided by non-bank intermediaries are characterized by greater innovation, lower costs, and enhanced convenience compared to traditional banking. According to the World Bank, fintech has enhanced the availability of financial services for persons with low incomes. From 2014 to 2017, the proportion of Indian adults who had bank accounts rose from 34% to 43%, partially attributable to the use of mobile money services (Financial Stability Board, 2019). The rise of neobanks and fintech startups in developed countries has led to increased competition in the banking sector, leading to better products and services for consumers.

Despite the enhanced accessibility and convenience it offers users, digital banking presents operational, cyber, data privacy, and regulatory threats. In order to achieve long-term success in a rapidly evolving financial environment, banks must effectively manage both innovation and risk.

Examining financial innovation in banking poses challenges due to the fast-paced evolution of the business and the complexities involved in quantifying its influence on bank performance. Differentiating financial innovation from other factors that influence bank performance, such as macroeconomic fluctuations and variations in customer behavior, can be challenging. Studying financial innovation is crucial for comprehending the dynamics of the banking system and finding methods for sustainable growth and competitiveness, despite the obstacles that may arise.

LIMITATIONS

In subsequent research, it is of the utmost importance to address the studies’ significant shortcomings. Initially, we conducted this analysis using data from a single nation, India. Because cultural influences can affect the significance of concepts such as perceived danger and service quality, it would be prudent for future studies to confirm the scale invariance of the instrument before applying it in various countries. Additionally, we selected individuals with prior experience with the online lending platform to participate in this research study. The study’s design limited the participants to those with prior experience using a digital lending platform. The conducted research indicates that as the familiarity and the ambiguity of the evaluated item diminish, the risk of common method bias increases. It is possible that this bias will have an effect on the factor structure. In order for researchers to reproduce the findings of this study, they need to employ samples that contain individuals with varying degrees of experience with digital lending platforms. It is also important to note that the early innovations in financial technology were primarily focused on government regulations, which included aspects such as industry standards and assurances of best practices. Research on digital lending platforms has primarily focused on the protection of consumers. We need to conduct further research on the regulation of components and the activities of associations to gain a deeper understanding of the goal of embracing digital lending platforms.

REFERENCES

• Adamek, J., & Solarz, M. (2023). Adoption factors in digital lending services offered by FinTech lenders. Oeconomia Copernicana, 14(1), 169-212. https://doi.org/10.24136/oc.2023.005
  » https://doi.org/10.24136/oc.2023.005
• Agarwal, S., & Chua, Y. H. (2020). FinTech and household finance: A review of the empirical literature. China Finance Review International, 10(4), 361-376. https://doi.org/10.1108/CFRI-03-2020-0024
  » https://doi.org/10.1108/CFRI-03-2020-0024
• Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888-918. https://doi.org/10.1037/0033-2909.84.5.888
  » https://doi.org/10.1037/0033-2909.84.5.888
• Armitage, C. J., & Conner, M. (2010). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40(4), 471-499. https://doi.org/10.1348/014466601164939
  » https://doi.org/10.1348/014466601164939
• Arun, T., Markose, S. M., Murinde, V., Kostov, P., Khan, A., Ari, N., Goel, V., & Sethi, R. (2023). Digital Lending in Fintech India - UK Comparison. SSRN. https://doi.org/10.2139/ssrn.4558960
  » https://doi.org/10.2139/ssrn.4558960
• Bagozzi, R. P., Baumgartner, J., & Yi, Y. (1989). An investigation into the role of intentions as mediators of the attitude-behavior relationship. Journal of Economic Psychology, 10(1), 35-62. https://doi.org/10.1016/0167-4870(89)90056-1
  » https://doi.org/10.1016/0167-4870(89)90056-1
• Balcázar, J. J. M., & Rivas, Á. E. L. (2021). Determining factors of the intention to adopt FinTech services by micro and small business owners from Chiclayo, Peru. Journal of Business, 13(2), 19. https://doi.org/10.21678/jb.2021.1650
  » https://doi.org/10.21678/jb.2021.1650
• Bank for International Settlements. (2018). BIS Annual Report. https://www.bis.org/about/areport/areport2019.htm
  » https://www.bis.org/about/areport/areport2019.htm
• Banking Technology Vision. (2023). Accenture. https://www.accenture.com/in-en/insights/technology/technology-trends-2023
  » https://www.accenture.com/in-en/insights/technology/technology-trends-2023
• Berg, T., Fuster, A., & Puri, M. (2022). FinTech lending. Annual Review of Financial Economics, 14, 187-207. https://doi.org/10.1146/annurev-financial-101521-112042
  » https://doi.org/10.1146/annurev-financial-101521-112042
• Berger, J. O., & Sellke, T. (1987). Testing a point null hypothesis: The irreconcilability of p values and evidence. Journal of the American Statistical Association, 82(397), 112-122. https://doi.org/10.2307/2289131
  » https://doi.org/10.2307/2289131
• Berry, L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of Marketing, 66(3), 1-17. https://doi.org/10.1509/jmkg.66.3.1.18505
  » https://doi.org/10.1509/jmkg.66.3.1.18505
• Bhattacharyya, D., Dietz, M., Edlich, A., Mehta, A., Weintraub, B., Höll, R., & Windhagen, E. (2023). Global Banking Annual Review 2023: The Great Banking Transition. https://www.mckinsey.com/industries/financial-services/our-insights/global-banking-annual-review
  » https://www.mckinsey.com/industries/financial-services/our-insights/global-banking-annual-review
• Brandon-Jones, A., & Kauppi, K. (2018). Examining the antecedents of the technology acceptance model within e-procurement. International Journal of Operations & Production Management, 38(1), 22-42. https://doi.org/10.1108/IJOPM-06-2015-0346
  » https://doi.org/10.1108/IJOPM-06-2015-0346
• Chopdar, P. K., Korfiatis, N., Sivakumar, V. J., & Lytras, M. D. (2018). Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the unified theory of acceptance and use of technology. Computers in Human Behavior, 86, 109-128. https://doi.org/10.1016/j.chb.2018.04.017
  » https://doi.org/10.1016/j.chb.2018.04.017
• Chulawate, N., & Kiattisin, S. (2023). Success factors influencing peer-to-peer lending to support financial innovation. Sustainabilty, 15(5), 4028. https://doi.org/10.3390/su15054028 [
  » https://doi.org/10.3390/su15054028 [
• Cornelli, G., Frost, J., Gambacorta, L., Rau, P. R., Wardrop, R., & Ziegler, T. (2023). Fintech and big tech credit: Drivers of the growth of digital lending. Journal of Banking & Finance, 148, 106742. https://doi.org/10.1016/j.jbankfin.2022.106742
  » https://doi.org/10.1016/j.jbankfin.2022.106742
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  » https://doi.org/10.2307/249008
• Dias, S., Nigalye, A., & Mahajan, J. (2022). Adoption factors of P2P lending in India. Atlantis Press. https://doi.org/10.2991/978-94-6463-080-0_11
  » https://doi.org/10.2991/978-94-6463-080-0_11
• Dorfleitner, G., Hornuf, L., Schmitt, M., & Weber, M. (2017). Definition of fintech and description of the fintech industry. In G. Dorfleitner, L. Hornuf, M. Schmitt, & M. Weber, FinTech in Germany (pp. 5-10). Springer. https://doi.org/10.1007/978-3-319-54666-7_2
  » https://doi.org/10.1007/978-3-319-54666-7_2
• DQINDIAOnline. (2021). Digital disruption: Is India ready for digital lending? https://www.dqindia.com/digital-disruption-india-ready-digital-lending/
  » https://www.dqindia.com/digital-disruption-india-ready-digital-lending/
• Financial Stability Board. (2019). BigTech in finance: Market developments and potential financial stability implications. https://www.fsb.org/2019/12/bigtech-in-finance-market-developments-and-potential-financial-stability-implications/
  » https://www.fsb.org/2019/12/bigtech-in-finance-market-developments-and-potential-financial-stability-implications/
• GSMA. (2023). State of the industry report on mobile money 2023. https://www.gsma.com/solutions-and-impact/connectivity-for-good/mobile-for-development/gsma_resources/state-of-the-industry-report-on-mobile-money-2023-2/
  » https://www.gsma.com/solutions-and-impact/connectivity-for-good/mobile-for-development/gsma_resources/state-of-the-industry-report-on-mobile-money-2023-2/
• Hai, P. T., Toan, L. D., Tam, V. V., & Thuong, M. T. (2017). Factors affecting customer satisfaction with the quality of services: empirical evidence at Da Nang International Airport, Vietnam. Asian Journal of Empirical Research, 7(3), 61-74. https://doi.org/10.18488/journal.1007/2017.7.3/1007.3.61.74
  » https://doi.org/10.18488/journal.1007/2017.7.3/1007.3.61.74
• Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3), 340. https://doi.org/10.3390/sym11030340
  » https://doi.org/10.3390/sym11030340
• Hubert, H., Blut, M., Brock, C., Zhang, R. W., Koch, V., & Riedl, R. (2018). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing, 53(6), 1073-1098. https://doi.org/10.1108/EJM-12-2016-0794
  » https://doi.org/10.1108/EJM-12-2016-0794
• Jin, L. Y., Osman, A. B., & Halim, M. S. B. AB. (2014). Perceived usefulness and trust towards consumer behaviors: A perspective of consumer online shopping. Journal of Asian Scientific Research, 4(10), 541-546. https://archive.aessweb.com/index.php/5003/article/view/3679
  » https://archive.aessweb.com/index.php/5003/article/view/3679
• Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111-122. https://doi.org/10.1016/j.chb.2017.10.035
  » https://doi.org/10.1016/j.chb.2017.10.035
• Kaji, S. (2021). An overview of fintech. In The Economics of Fintech (pp. 1-16). Springer. https://doi.org/10.1007/978-981-33-4913-1_1
  » https://doi.org/10.1007/978-981-33-4913-1_1
• Kalia, P., Kaushal, R., Singla, M., & Parkash, J. (2021). Determining the role of service quality, trust and commitment to customer loyalty for telecom service users: A PLS-SEM approach. The TQM Journal, 33(7), 377-396. https://doi.org/10.1108/TQM-04-2021-0108
  » https://doi.org/10.1108/TQM-04-2021-0108
• Ko, H., Jung, J., Kim, J., & Shim, S. W. (2004). Cross-cultural differences in perceived risk of online shopping. Journal of Interactive Advertising, 4(2), 20-29. https://doi.org/10.1080/15252019.2004.10722084Kumar, J. A., Bervell, B., Annamalai, N., & Osman, S. (2020). Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and whatsapp use habit. IEEE, 8, 208058-208074. https://doi.org/10.1109/ACCESS.2020.3037925
  » https://doi.org/10.1080/15252019.2004.10722084
• Kurniawan, R. (2019, August). Examination of the factors contributing to financial technology adoption in Indonesia using technology acceptance model: case study of peer to peer lending service platform. International Conference on Information Management and Technology (ICIMTech). https://doi.org/10.1109/ICIMTech.2019.8843803
  » https://doi.org/10.1109/ICIMTech.2019.8843803
• Lema, A. (2017). Factors influencing the adoption of mobile financial services in the unbanked population. Inkanyiso: Journal of Humanities and Social Sciences, 9(1). https://www.ajol.info/index.php/ijhss/article/view/165506
  » https://www.ajol.info/index.php/ijhss/article/view/165506
• Lien, C-H., Wen, M-J., Huang, L-C., & Wu, K-L. (2015). Online hotel booking: The effects of brand image, price, trust and value on purchase intentions. Asia Pacific Management Review, 20(4), 210-218. https://doi.org/10.1016/j.apmrv.2015.03.005
  » https://doi.org/10.1016/j.apmrv.2015.03.005
• Nguyen, H. V., Ha, G. H., Nguyen, D. N., Doan, A. H., & Phan, H. T. (2022). Understanding financial literacy and associated factors among adult population in a low-middle income country. Heliyon, 8(6), e09638. https://doi.org/10.1016/j.heliyon.2022.e09638
  » https://doi.org/10.1016/j.heliyon.2022.e09638
• Nguyen, Q. K., & Dang, V. C. (2022). The effect of FinTech development on financial stability in an emerging market: The role of market discipline. Research in Globalization, 5, 100105. https://doi.org/10.1016/j.resglo.2022.100105
  » https://doi.org/10.1016/j.resglo.2022.100105
• Nugraha, D. P., Setiawan, B., Nathan, R. J., & Fekete-Farkas, M. (2022). Fintech adoption drivers for innovation for SMEs in Indonesia. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 208. https://doi.org/10.3390/joitmc8040208
  » https://doi.org/10.3390/joitmc8040208
• Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40. https://psycnet.apa.org/record/1989-10632-001
  » https://psycnet.apa.org/record/1989-10632-001
• Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98(1), 194-198. https://doi.org/10.1037/a0030767
  » https://doi.org/10.1037/a0030767
• Praxis Global Alliance. (2023). Emerging opportunities for digital lending in India. https://www.praxisga.com/PraxisgaImages/ReportImg/emerging-opportunities-for-digital-lending-in-india-Report-3.pdf
  » https://www.praxisga.com/PraxisgaImages/ReportImg/emerging-opportunities-for-digital-lending-in-india-Report-3.pdf
• PricewaterhouseCoopers. (2021). Digital Banking Consumer Survey. https://www.pwc.com/us/en/industries/financial-services/library/digital-banking-consumer-survey.html#:~:text=This%20year%2C%20PwC’s%202021%20Digital,financial%20institutions%20of%20all%20sizes
  » https://www.pwc.com/us/en/industries/financial-services/library/digital-banking-consumer-survey.html#:~:text=This%20year%2C%20PwC’s%202021%20Digital,financial%20institutions%20of%20all%20sizes
• Putranto, B. D., & Sobari, N. (2021). Predicting Intention of Using Fintech Lending to Bank Users in Indonesia. Proceedings of the 18th International Symposium on Management (INSYMA 2021). https://doi.org/10.2991/aebmr.k.210628.034
  » https://doi.org/10.2991/aebmr.k.210628.034
• Putri, G. A., Widagdo, A. K., & Setiawan, D. (2023). Analysis of financial technology acceptance of peer to peer lending (P2P lending) using extended technology acceptance model (TAM). Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 100027. https://doi.org/10.1016/j.joitmc.2023.100027
  » https://doi.org/10.1016/j.joitmc.2023.100027
• Rahman, M., Lesch, M. F., Horrey, W. J., & Strawderman, L. (2017). Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accident Analysis & Prevention, 108, 361-373. https://doi.org/10.1016/j.aap.2017.09.011
  » https://doi.org/10.1016/j.aap.2017.09.011
• Raza, S. A., Umer, A., & Shah, N. (2017). New determinants of ease of use and perceived usefulness for mobile banking adoption. International Journal of Electronic Customer Relationship Management, 11(1), 44-65. https://doi.org/10.1504/IJECRM.2017.086751
  » https://doi.org/10.1504/IJECRM.2017.086751
• Rosavina, M., Rahadi, R. A., Kitri, M. L., Nuraeni, S., & Mayangsari, L. (2019). P2P lending adoption by SMEs in Indonesia. Qualitative Research in Financial Markets¸11(2), 260-279. https://doi.org/10.1108/QRFM-09-2018-0103
  » https://doi.org/10.1108/QRFM-09-2018-0103
• Rudra, T. (2022, July 01). Collaboration between fintech players & banks to transform SMB lending: Indifi’s Alok Mittal. Inc42. https://inc42.com/buzz/collaboration-between-fintech-players-banks-to-transform-smb-lending-indifis-alok-mittal/
  » https://inc42.com/buzz/collaboration-between-fintech-players-banks-to-transform-smb-lending-indifis-alok-mittal/
• Ryu, H-S. (2018). What makes users willing or hesitant to use Fintech?: The moderating effect of user type. Industrial Management & Data Systems, 118(3), 541-569. https://doi.org/10.1108/IMDS-07-2017-0325
  » https://doi.org/10.1108/IMDS-07-2017-0325
• Ryu, H-S., & Ko, K. S. (2020). Sustainable development of fintech: Focused on uncertainty and perceived quality issues. Sustainabilty, 12(18), 7669. https://doi.org/10.3390/su12187669
  » https://doi.org/10.3390/su12187669
• Schierz, P. G., Schilke, O., & Wirts, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209-216.
• Singh, N., & Sinha, N. (2020). How perceived trust mediates merchant’s intention to use a mobile wallet technology. Journal of Retailing and Consumer Services, 52, 101894. https://doi.org/10.1016/j.jretconser.2019.101894
  » https://doi.org/10.1016/j.jretconser.2019.101894
• Sunardi, R., Hamidah, H., Buchdadi, A. D., & Purwana, D. (2022). Factors determining adoption of fintech peer-to-peer lending platform: An empirical study in Indonesia. Journal of Asian Finance, Economics and Business, 9(1), 43-51. https://doi.org/10.13106/jafeb.2022.vol9.no1.0043
  » https://doi.org/10.13106/jafeb.2022.vol9.no1.0043
• Sunardi, R., Purwana, D., Suhud, U., & Hamidah, H. (2021). Examining the factors contributing to fintech peer-to-peer lending adoption. Journal of Information Systems Engineering and Business Intelligence, 7(2), 91-101. https://doi.org/10.20473/jisebi.7.2.91-101
  » https://doi.org/10.20473/jisebi.7.2.91-101
• Sweeney, J. C., Soutar, G. N., & Johnson, L. W. (1999). The role of perceived risk in the quality-value relationship: A study in a retail environment. Journal of Retailing, 75(1), 77-105. https://doi.org/10.1016/S0022-4359(99)80005-0
  » https://doi.org/10.1016/S0022-4359(99)80005-0
• Teo, T., Ursavaş, Ö. F., Bahçekapili, E. (2011). Efficiency of the technology acceptance model to explain pre-service teachers’ intention to use technology: A Turkish study. Campus-Wide Information Systems, 28(2), 93-101. https://doi.org/10.1108/10650741111117798
  » https://doi.org/10.1108/10650741111117798
• The Business Research Company. (2023). Peer-to-Peer (P2P) lending global market report 2024. https://www.researchandmarkets.com/report/peer-to-peer-lending?srsltid=AfmBOopBrBFn0_VTokDcuwR_0d-3GQFflSYZdgJjvsKi-BE6iUCSVZ9b
  » https://www.researchandmarkets.com/report/peer-to-peer-lending?srsltid=AfmBOopBrBFn0_VTokDcuwR_0d-3GQFflSYZdgJjvsKi-BE6iUCSVZ9b
• The Digital Fifth. (2023). India’s digital lending landscape - July’23. https://thedigitalfifth.com/key-ecosystems-within-indian-fintech-sector/
  » https://thedigitalfifth.com/key-ecosystems-within-indian-fintech-sector/
• Trafimow, D., Sheeran, P., Lombardo, B., Finlay, K. A., Brown, J., & Armitage, C. J. (2004). Affective and cognitive control of persons and behaviours. British Journal of Social Psychology, 43(2), 207-224. https://doi.org/10.1348/0144666041501642
  » https://doi.org/10.1348/0144666041501642
• Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872
  » https://doi.org/10.1287/isre.11.4.342.11872
• Xie, J., Latif, Z., Jianqiu, Z., & Waraa, K. (2019, December). Analysis of influencing factors of internet lending adoption in China: Internet lending adoption in China. IEEE. https://doi.org/10.1109/MACS48846.2019.9024778
  » https://doi.org/10.1109/MACS48846.2019.9024778
• Xie, J., Ye, L., Huang, W., & Ye, M. (2021). Understanding FinTech platform adoption: Impacts of perceived value and perceived risk. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1893-1911. https://doi.org/10.3390/jtaer16050106
  » https://doi.org/10.3390/jtaer16050106
• Yang, Q., & Lee, Y.-C. (2016, August). Critical factors of the lending intention of online P2P: Moderating role of perceived benefit. Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World. https://doi.org/10.1145/2971603.2971618
  » https://doi.org/10.1145/2971603.2971618
• Zhao, H., Ge, Y., Liu, Q., Wang, G., Chen, E., & Zhang, H. (2017). P2P lending survey: Platforms, recent advances and prospects. ACM Transactions on Intelligent Systems and Technology, 8(6), 1-28. https://dl.acm.org/doi/10.1145/3078848
  » https://dl.acm.org/doi/10.1145/3078848

• Funding:

  The authors stated that there was no funding for the research in this article.

• Data Availability:

  Asamani A., & Majumdar, J. (2024). An empirical study of digital lending in India and the variables associated with its adoption. Mendeley Data. https://doi.org/10.17632/syb6twd96n.1. BAR - Brazilian Administration Review encourages data sharing but, in compliance with ethical principles, it does not demand the disclosure of any means of identifying research subjects.

• Plagiarism Check:

  BAR maintains the practice of submitting all documents received to the plagiarism check, using specific tools, e.g.: iThenticate.

• Peer review:

  is responsible for acknowledging an article’s potential contribution to the frontiers of scholarly knowledge on business or public administration. The authors are the ultimate responsible for the consistency of the theoretical references, the accurate report of empirical data, the personal perspectives, and the use of copyrighted material. This content was evaluated using the double-blind peer review process. The disclosure of the reviewers’ information on the first page is made only after concluding the evaluation process, and with the voluntary consent of the respective reviewers.

• JEL Code:

  C12, G23, G51, O33

Edited by

Editors-in-Chief:

Ivan Lapuente Garrido https://orcid.org/0000-0003-3741-7961 (Universidade do Vale do Rio dos Sinos, Brazil)

Ricardo Limongi https://orcid.org/0000-0003-3231-7515 (Universidade Federal de Goiás, Brazil)

Associate Editor:

Esin Yoruk https://orcid.org/0000-0001-5795-2369 (Coventry University, United Kingdom)

Edited by

Editorial assistants:

Eduarda Anastacio and Simone Rafael (ANPAD, Maringá, Brazil).

Data availability

Asamani A., & Majumdar, J. (2024). An empirical study of digital lending in India and the variables associated with its adoption. Mendeley Data. https://doi.org/10.17632/syb6twd96n.1. BAR - Brazilian Administration Review encourages data sharing but, in compliance with ethical principles, it does not demand the disclosure of any means of identifying research subjects.

Publication Dates

• Publication in this collection
  04 Oct 2024
• Date of issue
  2024

History

• Received
  06 Feb 2024
• Accepted
  22 Aug 2024
• Published
  10 Sept 2024