Understanding the Factors Influencing Actual Usage of Payments Banks: An Empirical Investigation Using the Extended Information Systems Success Model

Abstract

This study aims to elucidate the factors that affect users’ intention to use payments bank services in India. The research model focuses on closing the gap between the intention to use and the actual usage of payments banks by integrating various quality dimensions with the trust and reputation of service provider. The research model was tested and validated using the partial least squares, which was performed on the data of 393 Indian users. SmartPLS software version 3.2.8 has been used in the current study. The results revealed that satisfaction and intentions to use have a significant effect on actual usage. The reputation of service provider, service quality and trust are the major constructs that significantly affect satisfaction and users’ intention to use payments banks. To make the consumers adopt the payments banks, they need to be satisfied, which can further lead to the actual usage. As payments banks are at a nascent stage, the reputation of service provider and trust are the most influential antecedents to the satisfaction and intention of users. Additionally, it is very pertinent for the service providers to use modern and safe technology to enhance their trust. Furthermore, personalized services for grievances redressal should be used to ensure the proper service and information being delivered to the user.

Keywords

Actual use payments banks trust satisfaction reputation of service provider IS success model

Introduction

In the last few years, the banking sector has witnessed remarkable changes in its processes, procedures and products (Gupta et al., 2019). These significant transformations in the banking sector have helped in delighting the customers by providing online banking. With the penetration of mobile technologies in the financial and banking sectors, the industry has enabled itself to provide services like bill payments and account transfers to purchase goods and services (Bashir & Madhavaiah, 2014; Damle et al., 2016; Srinivas, 2017). But regardless of these revolutionary benefits, the adoption of mobile banking and payments banks is still low in many parts of the country (Kaur et al., 2021; Mittal et al., 2017). In India, half of the total population is still out of the purview of online banking due to its limited access, lack of knowledge and perceived risks. People find traditional banking methods (like cash) more reasonable and interactive against online methods due to the underdeveloped environment of digital payments (Gupta, 2016).

To minimize the dependence of consumers on cash or traditional methods of banking, the Reserve Bank of India (RBI) came up with payments banks, a new digital payment solution (Bhansali et al., 2018; Kamruddin & Sultana, 2018; Naik et al., 2018). Payments banks are specialized banks recommended by the Nachiket Mor Committee of RBI, to provide multiple banking solutions and to improve financial inclusion in the country (RBI, 2020; Reddy, 2018). Payments banks offer many services which are equivalent to that of physical banking. Payments banks were launched in India to make payments banks an alternative over physical banking for the underbanked and unbanked (Gupta, 2016; RBI, 2020). The objective was to target the unbanked (not having any mainstream bank account) and underbanked population (who uses bank accounts rarely) of the country to provide simple and complete banking solutions (Kaur et al., 2021; Naik et al., 2018).

Although payments banks are accessed through mobile apps using the internet, they hugely differ from internet banking and other mobile banking services. Payments banks are different based on their concept from mobile and internet banking (RBI, 2014). Mobile banking/internet banking can be defined as a service offered by commercial banks with a 100% physical presence that allows their customer to carry out various banking operations through mobile devices (Alalwan et al., 2017). Whereas payments banks are differentiated banks with no physical presence, and thereupon, they operate through digital platforms. Studies reported that customers are adopting the digital solutions such as self-service kiosks, automated teller machine, machines for money deposits and passbook printing offered by commercial banks (Purohit & Mishra, 2017). However, the scenario is quite different in the case of payments banks as compared to the digital services offered by the traditional banking systems as payments banks have no physical presence. This physical absence of the payments banks makes the customers feel hesitant to fully adopt and use the payment bank as a complete alternative to traditional banking since financial decision-making is crucial in nature.

Yet, India has witnessed tremendous growth of digital payments from 2.4 digital transactions per capita per annum to about 22 from the year 2014–2015 to 2018–2019 (RBI, 2017/19) via Immediate Payment Service, BHIM Unified Payments Interface, wallets/prepaid payment instruments. Moreover, 970 million people in India have mobiles with internet facilities, and past trends denote a great scope for the entry of payments banks in energizing financial inclusion (Naik et al., 2018). But the adoption of payments banks remained lower than expected (Srinivas, 2017). Additionally, India’s main challenge for the triumph of the payments banks is to persuade the customers to utilize it as a complete alternative for cash and conventional banking (Vaishnavi & Shruthi, 2017). Very few studies to date have addressed this issue of low adoption of payments banks (Agarwal, 2016; Sikdar & Kumar, 2016; Sarkar & Rakshit, 2021; Rautela et al., 2021).

Consequently, the current study intends to explore the factors that may influence the actual usage of payments banks by using an extended information success (IS) model. For achieving this objective, the trust and reputation of service provider have been added to the IS model while controlling other factors to investigate its role in enhancing the adoption of payments banks.

Thus, the article is organized as follows: The first section deals with the overview of the study. The second section explains the theoretical background by focusing on the components of the IS model, task-technology fit model (TTF) and unified theory of acceptance and usage of technology (UTAUT). Further, the third section depicts the research methodology of the study, followed by the detailed results with the measurement model and testing of the hypotheses in the fourth section. The fifth section demonstrates the final discussion of the study, followed by the sixth, seventh and eighth sections depicting implications, conclusion and limitations of the study, respectively.

Literature Review

Payments Banks in India

In India, the economy is experiencing a shift towards digital banking platforms, and as a result, the government has introduced payments banks to facilitate and cover a larger customer base. Six payments banks are currently operating in India, namely Paytm Payments Bank, Airtel Payments Bank, Aditya Birla Idea Payments Bank, Jio Payments Bank, Fino Payments Bank and India Post Payments Bank. These banks are expected to transform the banking industry in the same manner e-commerce had revolutionized the retail sector while providing large services at low cost by altering the existing standards (Kaur et al., 2021; Shivnani, 2017).

Due to the lack of studies on payments banks, the theoretical background of the present study is dependent upon the related concepts like internet banking (Bashir & Madhavaiah, 2015), m-banking (Laukkanen, 2016; Vyas et al., 2016), m-wallets/mobile payments (Kaur et al., 2021; Shankar et al., 2018; Sinha, 2019; Usman et al., 2021). From the existing literature, articles have been carefully selected from Scopus and Google Scholar databases. Various keywords, namely internet banking, payments banks, electronic banking, mobile banking, mobile wallets, technology adoption and continued usage were used for searching relevant articles related to the current study.

Theoretical Background

Substantial work has been done on the IS model (DeLone & McLean, 1992) while combining it with other models such as TTF (Tam & Oliveira, 2016) and UTAUT (Gupta et al., 2019). The researchers have also integrated various constructs, that is, perceived risks (Bashir & Madhavaiah, 2015), perceived benefits (Gao & Waechter, 2017) and trust (Hsu et al., 2014) to examine the actual usage of the mobile banking services. Trust is a significant component in electronic banking, as it is connected with website characteristics and users’ natural trust characteristics. Trust among users provides a guarantee that they will acquire their expected utility, because if they do not trust payment bank providers, they may feel that there is the lack of ability, integrity or benevolence which will decrease their perceived usefulness (Gefen et al., 2003; Sarkar & Rakshit, 2021).

Additionally, in the present study, the model has been extended further by including the ‘reputation of service provider’, which is an important aspect in determining the intentions towards payments banks. Reputation is also a trust signal which is measured as a salient factor affecting initial trust. Thus, for purely online services like payments banks, the reputation of service provider plays a key role in providing initial stimulus to adopt a new service. Therefore, the role of reputation is studied to explain the adoption of services like payments banks which are having no physical presence.

System Quality

In internet banking, customers require an online storefront to use the device for the services, and thus, system quality enables the customers to use the banking services. In the internet banking atmosphere, availability, usability, reliability, response time and adaptability are the required characteristics to operate the system smoothly. These characteristics reflect how much a system is comfortable for the users (Zhou, 2013). Lack of these characteristics may result in developing doubts regarding the capacity and integrity of the service provider. Thus, customers may feel sceptical about the ability of the service provider to offer an adequate quality. A poor-quality system will never be able to provide its customer with a better user experience and satisfaction (Hsu et al., 2014). Thus, it can be considered that a good-quality system will positively amend the intentions of the users. It can be hypothesized that:

H_1a: System quality positively influences the intentions of users to use payments banks.

H_1b: System quality has a positive influence on the satisfaction of users.

Information Quality

Information quality integrates the various system characteristics, that is, sufficiency, timeliness, significance and precision (Sharma et al., 2017). In the milieu of e-banking, information quality encompasses the issues related to content. A system should be relevant, tailored, inclusive, secure and uncomplicated to understand. Zhou (2013) explained that the majority of customers use mobile payments services to pay their bills and expect to acquire relevant, correct, important, complete, useful and clear information. Furthermore, Veeramootoo et al. (2018) also considered the ‘information quality’ as an important construct that enhances users’ satisfaction and instigates them to use the site regularly. These considerations have a grave impact on the behavioural beliefs of a person, which ultimately may shape their intentions to use payments banks, failing to which will give rise to operational difficulties. Thus, the following hypotheses have been formulated:

H_2a: Information quality positively influences the intentions of users to use payments banks.

H_2b: Information quality has a positive influence on the satisfaction of users.

Service Quality

Service quality refers to ‘the quality of the support delivered by internet service providers to the users’ (DeLone & McLean, 2003). It reflects receptiveness, personalization, empathy, consistency and assurance regarding the system underuse. Good service quality indicates the service provider’s benevolence and ability which will help in building trust in users (Sama et al., 2021). In contrast, poor service quality will affect the experiences of the users and ultimately will lead to lost sales (Zhou, 2013). Thus, service quality is emphasized by many researchers, for example, Pitt et al. (1995) and Hsu et al. (2014) who highlighted service quality as an effective measure of information systems to guarantee the quality of service provided by the provider. For example, if a mobile payment system becomes unstable and unreachable while paying bills, it will annoy the user and will affect the users’ satisfaction. Thereupon, subsequent hypotheses have been formulated:

H_3a: Service quality positively influences the intentions of users to use payments banks.

H_3b: Service quality has a positive influence on the satisfaction of users.

Trust

In the online and mobile banking framework, trust is regarded as ‘willingness to operate banking transactions via the internet and expecting the fulfilment of obligations by the banks irrespective of consumer’s operating skills and abilities regarding online banking’. It includes three beliefs: truthfulness, munificence and ability (Abdullah et al., 2018). Truthfulness refers that service providers will keep their promises and do not deceive the operators. Munificence means that the providers take care of the user’s interests but not just their concerns and benefits. Finally, ability refers to the knowledge and skills of the service providers that are necessary for fulfilling the tasks. Mobile banking or mobile payments involve greater risk and uncertainty than physical banking (Yiga & Cha, 2016). Therefore, it increases the user’s concerns regarding the security and privacy of sensitive information. Additionally, private information stored on a smartphone is also prone to hackers (Tiwari et al., 2021). Thus, these privacy and security problems will decrease the trust and confidence in the service providers and increase the perceived risks and uncertainties (Kumar & Gupta, 2020; Oliveira et al., 2014). Thus, trust is considered an important factor that affects user satisfaction and intentions to use a system as well.

H_4a: Trust positively influences the intentions of users to use payments banks.

H_4b: Trust has a positive influence on the satisfaction of users.

Reputation of Service Provider

A lot of time, the notion of reputation has been linked with the concept of brand equity (Aaker, 1991) or the organization’s credibility to its consumers. Usually, reputation is reflected as an outcome of the organization’s relational history within which it performs. Hyde and Gosschalk (2005) had a view that reputation might influence the perception of consumers regarding a company’s products. Consequently, it may affect customers’ intentions towards the service/product on offer (Garrouch, 2021). The company’s goodwill, integrity and ability depend highly upon its reputation. A decent reputation will assure mounting the trust of customers even when they do not have immediate knowledge of the company. Similarly, Lohse and Spiller (1998) demonstrated that a reputation of a firm critically moves the confidence of people in the firm, which ultimately will affect satisfaction. McKnight et al. (1998) argued that a company’s reputation is positively associated with the perceived capability of the respective firm, which helps in gaining favourable public confidence without the aforementioned business engagements. Hence, hypotheses are:

H_5a: Reputation of service provider positively influences the intentions of users to use payments banks.

H_5b: Reputation of service provider positively influences the satisfaction of payments banks users.

H_5c: Reputation of service provider positively influences the trust of payment bank users.

User Satisfaction and Intentions to Use

User satisfaction arises from the several interactions between a service provider and users. It is expressed as a significant means of measuring the customer’s opinions regarding an e-system and covers the holistic experiences of the customers (DeLone & McLean, 2003). On the other hand, ‘intention to use’ encompasses everything from the number of websites visits, duration of the stay and frequency of access to the execution of a particular transaction (DeLone & McLean, 1992).

He further suggested that the amount of user satisfaction can affect the degree of intention to use’ positively or negatively because the intention to use and user satisfaction are closely associated. Satisfaction includes a great degree of face validity; therefore, no one can refute the triumph of a system whose users claim that they like or are satisfied. Thus, the following hypotheses were formulated:-

H_6a: User Satisfaction positively influences the intentions of users’ to use payments banks.

H_6b: User Satisfaction positively influences the actual use of the payments banks.

Actual Usage

It is imperative to attract potential adopters, but at the same time, to keep hold of existing users is also very important to remain in momentum. Retaining a user is directly connected with the belief of the user towards a particular system. Previous literature on information systems has investigated that positive intentions of users towards a system indicate that there is a strong association between customers’ satisfaction, intentions to use and repurchase or post-purchase intention, namely actual usage or continuous usage (Kuo et al., 2009). If potential customers remain unsatisfied, they will not be interested in using a service again. Liu et al. (2011) have highlighted that the users will continue their use of mobile banking if they had an enjoyable experience for the first time. Zhou (2013) also explained that trust leads to flow, which helps in building the satisfaction of a user towards mobile banking while influencing their continuance intentions to use. Therefore, the hypothesis formulated is:

H₇: Intention to use positively influences the actual usage of the payments banks.

Control Variables

In the present study, an extended IS success model has been used, which integrated the ‘reputation of service provider’ to the existing IS success model (Sharma & Sharma, 2019) to understand the usage of payments banks. For this purpose, the effect of some very well explored variables has been controlled in the study to unveil the effect of an ultimate independent variable (reputation of service provider). Thus, to validate the proposed model, the study controls for the effect of ‘system quality’, ‘service quality’, ‘information quality’ and ‘trust’. To avoid the loss of generality, these (control) variables have been taken as antecedents to all of the dependent variables in the model, and thus, they are controlled.

The proposed research model with its hypothesis is presented in Figure 1.

Figure 1.

Source: The author.

Research Methodology

Measurement

Current users of payments banks have been taken as the target population for this study, conducted in India (Punjab), where the majority resides in rural areas that have less access to physical banking services. Payments banks support diverse financial services that can be used any time over an extensive geographical area via mobile phones. Thus, the present study regarding payments banks justifies its need in the selected area as payments banks were launched primarily to cater to the needs of unbanked and underbanked people. All the items used for measuring the responses of the respondents regarding payments banks were adopted from the previous scales by making slight modifications (Table A1).

Data

The research instrument was formerly developed in English according to previous literature. However, after finalizing the instrument, translations were done into two native languages, that is, Punjabi and Hindi, by the professionals. The back-translation technique was employed and was finally checked for discrepancies to ensure the convergence equivalence (Brislin, 1970). The data collection was conducted between October 2019 and March 2020 using convenience sampling techniques. Convenience sampling, which is a non-random technique, has been used because it was not possible to create a sampling frame. Only users of payments banks were considered for the current study, and non-users were excluded at the initial stage from the survey. A total of 700 questionnaires were distributed and 419 were received back, from which only 393 were considered for further analysis, while the rest were dropped because of missing responses. A 5-point Likert scale was used to record the responses, ranging from highly disagree (1) to highly agree (5). The pilot study was conducted before actually administering the questionnaires to respondents. The questionnaires were distributed to 50 respondents for testing the reliability and validity of the research instrument. The value of Cronbach’s alpha was 0.81, which ensures the reliability of the research instrument.

For the determination of minimum sample size, G*power version 3.1.9 was used. The actual power of 0.95 with an effect size of 0.05 was achieved with a minimum sample of 262 (Figure 2) (Faul et al., 2009). However, a sample of 393 respondents was used in the current study which satisfies the minimum sample size requirements.

Figure 2.

Source: G*Power 3.1.9.

Non-response bias was also checked. Responses collected were distributed into two groups: early and late respondents. The Kolmogorov–Smirnov (K-S) test was then used to compare the distributions of both the groups (Ryans, 1974). The null hypothesis was accepted (p > .01), which states that there is no difference between the two groups and signifies the absence of non-response bias.

From the sample collected, the majority of the respondents were male (70.7%), and more than half of the population were from rural areas. Detailed demographic statistics are given in Table 1.

Table 1.

Sample Demographics

Distribution (n = 393)	Frequency	Percentage
Gender
Male	278	70.7
Female	115	29.3
Area
Urban	171	43.5
Rural	222	56.5
Age
20–30	177	45.0
31–40	120	30.5
41–50	79	20.1
More than 51	17	4.4
Education
Up to 10th	179	45.5
Up to 12th	135	34.3
Up to bachelors	72	18.3
Above bachelors	7	1.7
Income per annum (in lac)
Less than 1	12	3.0
1–3	173	44.0
3–5	124	31.5
More than 5	84	21.3

Source: Author’s calculation.

Results

Structural equation modelling (SEM) was applied further for analysing the model by employing partial least squares (PLS) because the distribution of the data collected was not normal (Chin et al., 2003; Kumar et al., 2020); second, PLS-SEM can handle more complex models having multiple constructs and indicators (Sharma et al., 2017). Third, for attaining the sufficient power of 0.80, a sample larger than 10 times the maximum number of inner or outer model links targeted towards any latent variable (Goodhue et al., 1998). The collected sample met the conditions discussed above for using PLS, and version 3.2.8 was used. All the values of variance inflation factor (VIF) were also below the level of 3.3, which indicated that the model is free from any pathological co-linearity and common methods bias (Table 2).

Table 2.

VIF Values

Constructs	Actual Usage	Information quality	Intention to use	Service Quality	Satisfaction	System Quality	Trust
Actual Usage	–	–	–	–	–	–	–
Reputation of service provider	–	–	1.887		1.867		1.001
Information quality	–	–	2.639	–	–	–	–
Intention to use	1.012	–	–	–	–	–	–
Service quality	–	–	1.109	–	–	–
Satisfaction	1.012	–	2.633	–	–	–
System quality	–	–	1.015	–	1.015
Trust	–	–	1.108	–	1.108

Source: Compiled from SmartPLS 3.2.8.

Measurement Model

The validity and reliability of the model were further accessed. For reliability, composite reliability (CR) and Cronbach’s alpha were examined. Cronbach’s alpha was greater than 0.7, which is considered to be satisfactory, and the value of CR of all the constructs was above 0.7, which signifies that the model is internal consistency (Table 4) (Henseler et al., 2009).

For ensuring the validity of the constructs, convergent and discriminate validity was accessed. The former ensures that all the items converge to only one single construct, and the latter signifies that the constructs are statistically different from one and another. To ensure convergent validity, the factor loadings of all the constructs should be more than 0.7, which ensures a high correlation of the items with the construct. Second, average variance extracted (AVE) should also be greater than 0.5 and cross-loadings. All the conditions were met, and the results are represented in Tables 3 and 4 (Hair et al., 2016).

Table 3.

Cross Loadings

Items	actU		infoQ	intU	srvQ	stsf	sysQ	Trst
Actual Usage 1	0.977	0.082	0.121	0.271	0.101	0.122	0.01	0.158
Actual Usage 2	0.927	0.105	0.108	0.95	0.094	0.104	0.004	0.143
Actual Usage 3	0.984	0.071	0.125	0.387	0.095	0.121	0.002	0.165
Reputation of service provider 1	0.091	0.982	0.050	0.050	0.145	0.049	0.563	0.043
Reputation of service provider 1	0.092	0.976	0.021	0.051	0.132	0.017	0.594	0.032
Reputation of service provider 1	0.056	0.947	0.037	0.022	0.163	0.035	0.469	0.016
Reputation of service provider 1	0.033	0.908	0.015	0.002	0.175	0.027	0.305	0.011
Information quality 1	0.106	0.056	0.973	0.097	0.027	0.277	0.045	0.012
Information quality 2	0.111	0.053	0.965	0.105	0.047	0.071	0.046	0.014
Information quality 3	0.124	0.045	0.937	0.116	0.039	0.233	0.045	0.006
Information quality 4	0.127	0.016	0.923	0.122	0.047	0.109	0.038	0.026
Intention to use 1	0.486	0.050	0.108	0.991	0.094	0.104	0.004	0.143
Intention to use 2	0.387	0.043	0.107	0.994	0.097	0.105	0.002	0.144
Intention to use 3	0.483	0.048	0.119	0.981	0.099	0.115	0.017	0.16
Service quality 1	0.102	0.182	0.032	0.103	0.842	0.037	0.093	0.305
Service quality 2	0.098	0.078	0.04	0.097	0.919	0.043	0.055	0.28
Service quality 3	0.083	0.192	0.047	0.083	0.884	0.053	0.103	0.302
Satisfaction 1	0.109	0.089	0.983	0.103	0.034	0.937	0.053	0.03
Satisfaction 2	0.097	0.024	0.956	0.088	0.047	0.968	0.037	0.005
Satisfaction 3	0.134	0.008	0.856	0.127	0.051	0.948	0.01	0.003
System quality 1	0.01	0.096	0.033	0.007	0.084	0.025	0.851	0.095
System quality 2	0.002	0.166	0.031	0	0.089	0.025	0.975	0.103
System quality 3	0.001	0.170	0.014	0.002	0.09	0.005	0.842	0.099
System quality 4	0.011	0.099	0.057	0.01	0.079	0.048	0.922	0.083
Trust 1	0.17	0.050	0.023	0.165	0.299	0.02	0.098	0.974
Trust 2	0.158	0.016	0.009	0.153	0.311	0.003	0.113	0.901
Trust 3	0.124	0.001	0.002	0.115	0.276	0.003	0.056	0.888

Source: Compiled from SmartPLS 3.2.8.

Note: Bold values represent factor loading.

To examine discriminant validity, two methods were used. Initially, diagonal elements, that is, the square roots of AVE’s should be more than the off-diagonal lower elements, that is, a correlation value of each pair of constructs. Additionally, the intended loadings should be greater than 0.7 and cross-loadings should be below 0.4 (Fornell & Larcker, 1981; Kumar et al., 2020). In Table 4, it can be observed that the value of the square root of AVE is higher than the corresponding values of the correlation. The measurement model result indicates good indicator reliability, internal consistency, discriminant and convergent validity of the model. The constructs of the model are statistically distinct, and hereafter, they will be used to test the structural model.

Table 4.

Reliability and Validity

Construct	Cronbach’s Alpha	Composite Reliability	AVE	Actual Usage	Reputation of Service Provider	Information Quality	Intention to Use	Service Quality	Satisfaction	System Quality	Trust
Actual usage	0.99	0.903	0.981	0.990
Reputation of service provider	0.977	0.876	0.909	0.088	0.954
Information quality	0.986	0.899	0.961	0.119	0.046	0.98
Intention to use	0.991	0.894	0.982	0.494	0.047	0.112	0.991
Service quality	0.971	0.911	0.945	0.098	0.148	0.041	0.098	0.972
Satisfaction	0.964	0.926	0.932	0.117	0.044	0.096	0.109	0.045	0.966
System quality	0.983	0.905	0.943	0.003	0.658	0.044	0.004	0.085	0.036	0.971
Trust	0.974	0.883	0.950	0.157	0.019	0.012	0.151	0.304	0.008	0.094	0.975

Source: Compiled from SmartPLS 3.2.8.

Note: Bold values represent squared AVE.

Testing of Hypothesis

The path coefficient for the model is derived from the standard error with bootstrapping t-statistics with 5,000 iterations (Hair et al., 2016). All the hypotheses except H_1a and H_2a are statistically significant at a 5% level of significance. Two models were assessed separately; initially, the model was assessed with only control variable, and at a later stage, the construct of reputation of service provider was included as well to compare the results in terms of predictive power (Figure 2). The results of the structural model can be summarized as follows.

The results indicate that the reputation of service provider, system quality, service quality, information quality and trust play a significant role in determining satisfaction and users’ intention to use payments banks. Further, satisfaction and intention to use determine the actual usage of payments banks.

The reputation of service provider significantly affects satisfaction (β = 0.601, p < .05), intention to use (β = 0.121, p < .05) and trust (β = 0.301, p < .05). Thus, hypotheses H_5a, H_5b and H_5c stand accepted. System quality (β = 407, p < .05), information quality (β = 468, p < .05), service quality (β = 511, p < .05) and trust (β = 509, p < .05) are statistically significant in explaining the satisfaction of users derived from payments banks. Thus, hypotheses H_1b, H_2b, H_3b and H_4b are confirmed.

The constructs of service quality (β = 352, p < .05) and trust (β = 537, p < .05) are found to be statistically significant in explaining the intentions of the users to use payments banks. Thus, hypotheses H_3a and H_4a are confirmed. Contrarily, hypotheses H_1a and H_2a were rejected as system quality and information quality are found to be statistically insignificant in explaining the intentions to use payments banks.

Satisfaction is having a significant effect on the intention to use and actual usage, with the former having β = 301, p < .05 and the latter having β = 308, p < .05, thus confirming hypotheses H_6a and H_6b. Also, the effect of intention to use is found to be significant (β = 273, p < .05) on actual usage. The complete results are shown in Table 5.

Table 5.

Path Coefficients

Hypothesis	Paths	Standardized Path Coefficients	t-Statistics (\|O/STDEV\|)	Decision
H _1a	sysQ→ intU	0.009	1.021	Reject
H _1b	sysQ→ stsf	0.407	35.66	Accept
H _2a	infoQ→ intU	0.112	1.10	Reject
H _2b	infoQ→ stsf	0.468	36.64	Accept
H _3a	srvQ→ intU	0.352	28.54	Accept
H _3b	srvQ→ stsf	0.511	45.46	Accept
H _4a	trst→ intU	0.537	33.67	Accept
H _4b	trst→ stsf	0.509	45.56	Accept
H _5a	frp→ intU	0.121	15.65	Accept
H _5b	frp→ stsf	0.601	50.12	Accept
H _5c	frp→ trst	0.301	21/45	Accept
H _6a	stsf→ actU	0.308	21.67	Accept
H _6b	stsf→ intU	0.301	28.87	Accept
H ₇	intU→ actU	0.293	22.56	Accept

Source: Compiled from SmartPLS 3.2.8.

Notes: sysQ = system quality; infoQ = information quality; srvQ = service quality; frp = reputation of service provider; intU = intention to use; stst = satisfaction; trst = trust; actU = actual usage.

Additionally, f² effect size has been reported to confine the contribution of the individual exogenous construct upon the other endogenous construct. The values of f² effect size on the structural model of the study have been reported in Table 6. The effect size of the variables has been identified into various categories, namely small effect (effect > 0.020 and ≤ 0.150), medium effect (effect > 0.150 and ≤ 0.350) and large effect (effect > 0.350) by using cut-off values as suggested by Cohen (1988).

To further validate the predictive power of the model, Stone-Geisser’s Q² value was assessed (Geisser, 1974) by employing blindfolding. This measure represents the predictive quality of the model. All the values of Q² for endogenous variables were found to be positive. Positive Q² values indicate that the rudiments of the predictive power of the model are satisfied (Hair et al., 2016). The results are shown in Table 6.

Table 6.

Result of Hypothesis Testing

Hypothesis	f²	Effect Size	Support	R²	Q²
stsf (satisfaction)				0.802	0.864
H_2b: infoQ → stsf	0.056	Small	Accept
H_3b: srvQ → stsf	0.467	Large	Accept
H_1b: sysQ → stsf	0.190	Medium	Accept
H_4b: trst → stsf	0.290	Medium	Accept
H_5b: frp → stsf	0.417	Large	Accept

intU (intention to use)				0.602	0.229
H_2a: infoQ → intU	–	–	Reject
H_3a: srvQ → intU	0.090	Small	Accept
H_1a: sysQ→ intU	–	–	Reject
H_4a: trst→ intU	0.100	Small	Accept
H_5a: frp→ intU	0.105	Medium	Accept
H_6b: stsf→ intU	0.195	Medium	Accept

actU (actual use)				0.785	0.963
H_6a: stsf→ actU	0.316	Medium	Accept
H₇: intU→ actU	0.067	Small	Accept

trst (trust)				0.291	0.126
H_5c: frp→ trst	0.061	Small	Accept

Source: Compiled from SmartPLS 3.2.8.

Discussion

As per the outcomes of the statistical analysis, it is quite evident that the model used in the present study has achieved significant predictive power by the exogenous constructs. The results from the aforementioned section support the integration of ‘reputation of service provider’ in the research model by achieving satisfactory predictive power. The inclusion of reputation of service provider increased the value of R² in intention to use, satisfaction and actual usage from 53.8% to 60.2%, 73.6% to 80.2% and 76.8% to 78.5% (see Figure 3), respectively. The variances are explained by all dependent variables in an acceptable range, as suggested by Alalwan et al. (2017). The research model has managed to achieve higher predictive power than that in previous studies conducted on mobile banking and digital banking by using the IS model (Hsu et al., 2014; Sharma & Sharma, 2019). In the present study, the construct of ‘satisfaction’ is having a higher value of R² than the value of the construct of ‘intention to use’. In comparison, the results of previous studies conducted on mobile banking applications came up with different findings. Studies conducted by Chatterjee et al. (2018) and Dwivedi et al. (2013) reported that intention to use is having higher predictive power than satisfaction. This outcome divulges the prominent role played by satisfaction in enhancing the adoption of payments banks as opposed to the case of mobile banking.

Figure 3.

Source: SmartPLS 3.2.8.

Concerning the results presented in Table 5, ‘trust’ and ‘reputation of service provider’ are the key determinants affecting the satisfaction and intentions to use payments banks. As per the studies conducted by Chatterjee et al. (2018), DeLone and McLean (2003) and Dwivedi et al. (2013), quality dimensions have often played a more dominant role than ‘trust’ in the adoption process of mobile banking, whereas the ‘reputation of service provider’ and ‘trust’ have remained a dominant factor for payments banks. This finding implies that service providers need to build the reputation of service provider to attract customers. As payments banks are new, customers lack in having previous experiences, and as a result, payments banks should rely on massive advertisement campaigns and viral marketing to gain reputation and awareness. The reputation of service provider also affects trust, which implies that if a payment bank establishes a good reputation, customers’ trust will also be enhanced. Furthermore, service providers need to gain the trust of the users by offering adequate security while sharing sensitive information over the payment bank application.

Statistical results about ‘service quality’ revealed a significant effect on users’ intention to ‘use’ and ‘satisfaction’. The present finding implies that proper service should be given to the users to enhance their satisfaction and intention to use payments banks. As payments banks are operated only through online platforms, timely follow-up and proper grievance redressal systems should be put into place to enhance the satisfaction of the users. Additionally, service providers may resort to sharing virtual demonstrations to use the application and to log a complaint if needed. The outcome is in line with the studies conducted by Chatterjee et al. (2018), DeLone and McLean (2003) and Veeramootoo et al. (2018).

Further, empirical results also supported that system and information quality significantly influences the satisfaction of the user but has no significant effect on users’ intention to use payments banks. However, information quality has been found as the key determinant in previous studies such as Chatterjee et al. (2018), Dwivedi et al. (2013), Gefen (2002) and Veeramootoo et al. (2018) concerning the adoption intention of mobile banking, but the effect of information quality is not found promising as far as the adoption of payments banks is concerned. The outcome is opposite to the studies of Akter et al. (2013), Zhou (2013) and Oliveira et al. (2014) and is partially in coherence with the study conducted by Chatterjee et al. (2018). This outcome might have been the effect of alternate mobile banking applications (e.g., internet banking, SMS banking and e-wallets) which are already well established and are providing better information quality. Additionally, users are not concerned regarding the quality of the system offered in terms of easy usage, navigation and structure of a mobile application. The plausible explanation behind this can be the previous exposure of users and their confidence in operating mobile devices and applications. Thus, users are comfortable even while using a complex system of payment bank applications. Although system and information quality are insignificant towards usage intention, incorporating these dimensions in decision-making can result in providing some additional satisfaction and contentment to the users.

As for the role of satisfaction, it has a significant influence on the user’s intentions to use payments banks. This implies that higher satisfaction levels further help in building user intention towards payments banks. Thus, satisfaction by playing a prime role motivates the users to adopt payments banks which are measured as actual usage. This implies the importance of providing satisfaction to users by offering them a safe, secure and holistic experience.

Theoretical implications

From a theoretical perspective, this study extends the IS model with the trust and reputation of service provider to explain the adoption of payment banks. The results supported that the trust and reputation of service provider positively influence the adoption intention of users towards payments banks. While controlling other variables, the trust and reputation of service provider have resulted in an increase in R² of intention to use, satisfaction and actual usage from 53.8% to 60.2%, 73.6% to 80.2% and 76.8% to 78.5%, respectively. The contribution of the study is twofold. First, the study enhances the limited body of knowledge on payments banks. Most payments bank research focuses on potential adopters, whereas the current study extends the knowledge concerning the actual usage of payments banks. Second, the results depicted that the constructs of system, information quality and service quality influence the satisfaction of users, which in turn result in actual usage. The results indicate that the payments banks users are more likely to use payments banks if they feel satisfied while using it (Oliveira et al., 2016). On the other hand, information and system quality have no significant effect on actual usage. Thus, this model depicts the important role played by the construct of satisfaction in the adoption of purely online services like payments banks. Additionally, the influence of the reputation of service provider on the trust and satisfaction of users also reveals that users are concerned about the reputation of the service provider as well.

Practical Implications

The study offers various practical implications for practitioners and decision-makers. The focus of the service providers should be high on service quality and trust. As payments banks are at a nascent stage, reputation should be built by advertising the product and by providing efficient solutions to customers. Additionally, more effort should be made to develop a good relationship that offers trust and personal touch to the users. It is very pertinent for the service providers to use modern and safe technology so that it can result in enhancing trust. As the information is very confidential and important, a proper redressal mechanism should be established if in case any problem arises during the transaction. Service providers can carry out advertisement campaigns to make customers aware of the services on offer. Consequentially, this will also result in a gain in the reputation in the market.

Service providers in an attempt to reduce the cost often make decisions to outsource their services to call centres, which should be done with utmost care and without compromising the quality of services offered. Firms should ensure the proper delivery of services even if they have outsourced that to the call centres because poor handling of customers’ calls, long waiting periods and grievances can lead to the aggravation of users’ frustration, leading to negative intentions and bad word of mouth. Problems should be solved promptly to enhance the users’ experience. Information quality and system quality also enhance the satisfaction of users who tend to use payments banks. Thus, service providers are encouraged to offer hassle-free software platforms that are easy to use. In addition, guidelines to use the software along with virtual tours and ‘frequently asked questions’ should be added to facilitate the customers who are either less educated or less aware about the service usage. There should be continuous upgradation of user information on digital platforms to keep the user satisfied. Stale and inappropriate display of information can cause a chaotic situation, and this could harm the users’ satisfaction.

Thus, the service providers and policymakers should satisfy the users by providing a satiating experience based upon the aforementioned factors for continued usage in the future.

Conclusion

The present study aimed to understand the adoption and actual usage of the payments banks in India by using an integrative framework developed on the lines of IS model by incorporating the dimension of reputation of service provider. Payments banks are new for users, and they are operating without having any physical point of contact and offices. This poses some major challenges for payments banks which have been addressed in the current study. Also, the differences in the intentions of the users towards digital banking and applications and payments banks have been elucidated to cover the lacuna in the literature about the adoption of payments banks. PLS-SEM has been used in the study to validate the proposed hypothesis and to identify significant predictors. The results revealed that the reputation of service provider, service quality and trust are major predictors of users’ intention to use, whereas all dimensions significantly predict the endogenous construct of ‘satisfaction’. In a nutshell, it can be harangued that service providers should try to satisfy the users to maintain the continuous usage of the payments banks.

Limitations and Future Research Directions

The present study on the adoption of payments banks also has some limitations, which could leave scope for further explorations regarding adoptions of information systems. As the study was conducted in the state of Punjab, the results may have poor generalization to other geographical areas having a population with different demographic characteristics. Second, the study is cross-sectional in nature, and thereby, a longitudinal study can be conducted to explore the evolution of users’ behaviour towards payments banks. The effect of the various demographic factors was also not considered while assessing the behaviour, and this study could be taken further to assess the role of demographics in adoption. Additionally, further studies can be conducted which may focus on differing perceptions of users belonging from urban and rural areas. Finally, this study does not address the relationship of the current model with continued usage; thus, this model can be further extended to explore factors, resulting in the continued usage of payments banks.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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