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Abstract

The proliferation of mobile payment applications has brought about a transformative shift in the banking industry, with an increasing number of users integrating these platforms into their routine financial transactions. This study seeks to establish a research framework to empirically examine the interrelationships between quality dimensions, and customer experience satisfaction, and stickiness of mobile payment applications. Using the structural equation modelling (SEM) methodology, the research analyses data from a sample of 280 users who actively used mobile payment applications within the past three months. The findings reveal that both information quality and service quality exert a significant positive impact on customer experience with these application, whereas system quality does not demonstrate a comparable effect. Conversely, system quality emerges as the sole determinant significantly influencing the sustained use of these applications. These findings offer critical insights for application developers and marketers aiming to enhance the functional and experiential quality of mobile payment platforms.

Keywords

customer experience information system success model mobile payment applications mobile app stickiness

Introduction

Mobile payment applications represent a revolutionary force within the financial sector, changing the consumer relationship with everyday transactions, bill payments, and, in some cases, even investments. These apps are m-commerce tools that connect financial institutions and customers through a mobile platform. Various entities, such as banks, technology companies, and third-party payment processors, usually offer these apps. Mobile payment apps may simultaneously integrate with multiple financial institutions and payment networks to enable transactions across different platforms and providers. Mobile payment apps often require users to link their bank accounts or payment methods to facilitate transactions.

Academic researchers have long predicted that mobile payment apps would emerge as the future star or killer application in the mobile business landscape (Liébana-Cabanillas et al., 2014). Mobile apps have disrupted traditional payment systems, replacing the need for cash, credit cards, and cheques (Flavian et al., 2020), Users are increasingly drawn to their speed, convenience, and ability to personalize financial services, with features such as cashback and loyalty rewards contributing to widespread adoption. In India, the integration of UPI technology has further accelerated digital payments. The growing popularity has made mobile payment apps the primary financial tool for many users by supporting investment, insurance, and other services beyond facilitating payments. Their user-centric design and always available functionality make them preferable to traditional banking channels (Businessline, 2021).

The growth of mobile payment apps is catalysed by factors like smartphone penetration rates, internet connectivity, and the shifting expectations of consumers about convenient financial transactions. Payment applications are designed to deliver a personalized banking experience to customers (Karjaluoto et al., 2019). Surprisingly, some payment apps are able to track customers more intrinsically and provide opportunities to integrate loyalty programs in a more customized way, thereby enabling them to earn rewards, discounts, or cashback on most transactions. Thus, Mobile payment apps are altering the business model.

Despite extensive research into mobile payment adoption, much of the existing literature has primarily concentrated on factors influencing initial user decisions, such as perceived usefulness, trust, security, and ease of use (Tan et al., 2024; Yi et al., 2024). However, post-adoption behaviour, particularly those linked to customer experience and app stickiness, remain underexplored. While early adoption drivers are well-documented, they provide limited insight into the quality-driven experiences that encourage users to stay. These experiential factors are increasingly seen as foundational to driving higher-order outcomes such as share of wallet, customer lifetime value, and brand advocacy. Behera and Kumra (2024) emphasize the need to examine how sustained satisfaction and engagement translate into long-term loyalty and deeper value creation. Furthermore, existing studies often focus on narrow demographics or regions, overlooking broader experiential patterns and the complex interplay between emotional engagement, perceived service quality, and ongoing platform use (Tang & Tsai, 2024).

Many existing studies rely on frameworks like TAM, TPB, and UTAUT (Manser Payne et al., 2021; Naeem et al., 2022; Verkijika & Neneh, 2021). While useful for explaining adoption behaviour, these models often lack explanatory power for post-adoption phenomena like customer experience and stickiness. TAM emphasizes usefulness and ease of use; UTAUT highlights performance expectancy and social influence. However, they provide limited insight into the quality-driven experiences that encourage users to stay.

To overcome these limitations, this study adopts the Information Systems Success Model (DeLone & McLean), which incorporates system, information, and service quality as core dimensions. This model provides a robust structure for analysing how app quality affects customer satisfaction and app stickiness (Yi et al., 2024).

App stickiness refers to a user’s tendency to repeatedly use and remain loyal to a particular app, even when alternatives exist. Stickiness is often a precursor to loyalty and plays a crucial role in reducing churn. While some research (Sikkel & van Meer, 2015; Wu & Zha, 2025) acknowledges the importance of quality and security, few studies examine how these quality attributes directly influence post-adoption user experience and continued engagement.

This study addresses that gap by investigating how dimensions such as system quality, information quality, and service quality influence customer satisfaction and app stickiness in mobile payment applications. This approach responds to recent calls for more research into post-usage behaviour and loyalty (Behera & Kumra, 2024). Accordingly, the study aims to answer two key questions derived from these research gaps.

RQ1: Which characteristics of mobile payment apps predict customer experience satisfaction and app stickiness?

RQ2: How is customer experience satisfaction related to mobile payment app stickiness?

By exploring these questions, the study aims to provide practical insights for app developers and financial institutions. Understanding what drives customer satisfaction and stickiness can inform strategists to improve service quality, increase retention, and build long-term user loyalty.

From an academic perspective, this research contributes to the literature by focusing on post-adoption experience and extends previous work by applying the ISSM framework to mobile payment apps a context where user experience is a critical determinant of continued use. In doing so, the study offers a more comprehensive understanding of what sustains user engagement over time.

Literature review

Introduction to customer experience

The term “customer experience” defines the personal encounter with the service provider across different touchpoints (Edvardsson et al., 2010; Grønholdt et al., 2015; Kant & Jaiswal, 2017). Customer experience is created before the service is rendered while obtaining assistance and after the service is delivered. In addition, it includes all contacts made by the customer through alternative channels (Maklan & Klaus, 2011).

By setting themselves apart from their competitors and delivering a positive customer experience, businesses can increase customer satisfaction, trust, intention to revisit or repurchase, and loyalty (Chahal & Dutta, 2015; Komulainen & Saraniemi, 2019; McLean & Wilson, 2016). Therefore, every firm strives to provide a memorable experience. However, defining and conceptualizing the customer experience was tricky and not idealized. Previously, Pine and Gilmore proposed the notion of experience in marketing theory 1998 before proposing views of customer experience across different periods. Even though the measurement and definition of customer experience were not fully established, they sought to describe it in marketing literature as a distinct economic offering (Pine & Gilmore, 1998). Later, researchers such as (Schmitt, 1999), explained customer experience using human senses; according to the author, customer experience comprises five experience modules: Sense, Feel, Think, Act, and Relate. Following that, other authors have attempted to conceptualize customer experience by including dynamics, management strategies (Verhoef et al., 2009), and customer level of satisfaction (McLean & Wilson, 2016), particularly with the aid of integrating technology. Concerning technology advancements, numerous researchers have promptly proposed new perspectives on the customer experience centred around retail shopping (Arnold et al., 2005), online banking (Garg et al., 2014), tourism (Hwang & Seo, 2016), online shopping (Jaziri, 2019; Rose et al., 2011), and mobile apps (Mclean et al., 2018). In addition, we see most existing literature has utilized TAM, UTAUT, personal construct theory (PCT), and flow theory (FT) to explore customer experience (Bilgihan et al., 2016; Martinez & McAndrews, 2021) with limited focus on quality dimensions.

When moving further, we also found the primary antecedents of customer experience include brand performance, service interface interaction, price, promotions, a moment of truth, convenience, perceived value, trust, perceived usability, digital banking innovation, benefits, and technology adoption (S. Kim & Hyun, 2018; Mbama et al., 2018). Although customer experience research is expanding, it is still fragmented (McColl-Kennedy et al., 2015); especially there is a need to quantify customers experience satisfaction with their mobile apps’ interaction experience. For instance, Mbama and Mbama and Ezepue (2018) observed that one of the critical elements affecting customer experience is service quality; yet, when migrating to a digital platform other necessary dimension such as information and system quality need to be explored as devised by Delone and Mclean (2004).

Mobile payment apps and customer experience

The role of mobile payment systems in shaping customer experience has garnered significant attention, particularly given the rapid rise in mobile payment adoption in various regions. Spinelli et al. (2024) identify barriers to mobile payment adoption, such as concerns about security and perceived usefulness, suggesting that technology readiness plays a critical role in shaping customer behaviours toward these digital solutions. In a meta-analysis by Schomburgk et al. (2024), the “cashless effect” is identified, wherein consumers tend to spend more when using cashless payment methods. The cashless effect, however, varies across different consumption contexts and economic conditions. These findings are consistent with earlier research which established that cashless payment systems influence customer decision making through reduced “pain of paying” (Prelec & Simester, 2001). Islam et al. (2024) also highlighted that QR mobile payments are increasingly popular in emerging markets, providing greater convenience and efficiency in financial transactions.

Further complicating the customer experience is the development of multifunctional digital platforms, commonly known as super apps. Hasselwander (2024) describes how super apps like WeChat and Grab have evolved from single purpose applications to comprehensive platforms that serve multiple consumer needs, from payments to transportation. This evolution enhances the experience by offering increased convenience, thereby fostering greater engagement and satisfaction among users. Sharma et al. (2024) also emphasizes the importance of service quality in FinTech services, which plays a critical role in the utilization of super apps, particularly when integrated with technologies like the Unified Payments Interface (UPI).

Referring to our subject of measuring customer experience, goes beyond straightforward economic gain or a tactic that engages the customer’s senses. More significantly, a person’s interior mental state determines how long they will stay with a particular service or commodity. There is no denying that customer experience offers customers and companies a greater value proposition. However, measuring customer experience based on its outcomes is one of the finest ways to do so. Customer satisfaction is one of the outcomes that can be anticipated from the customer experience (Chahal & Dutta, 2015). As a result, combining customer experience satisfaction will offer more insightful information about consumer behaviour than other variables.

Stickiness and customer experience

The concept of stickiness has been widely studied in e-commerce and social commerce, where features such as rich functionality and social engagement play pivotal roles in capturing user attention and enhancing the overall customer experience (Friedrich et al. 2019). Social commerce elements like rating systems and social sharing tools have been shown to positively influence both cognitive and emotional user responses, boosting website stickiness and customer loyalty (Mikalef et al. 2017).

Stickiness is influenced by factors such as feature quality, ease of use, perceived value, and positive user experiences. Positive experiences not only enhance stickiness but also drive customer satisfaction, which, in turn, encourages continued app usage (Alnawas et al., 2023). Ali (2016), investigated the impact of website quality and perceived flow on customer satisfaction and purchase intentions in the hospitality industry, highlighting how high quality websites can induce a state of flow that fosters loyalty and stickiness.

Stickiness has been examined across various contexts, including social networking sites (Shao et al., 2020), mobile apps (Nandi et al., 2021), health apps(Carlo et al., 2020), m-wallets (Matemba et al., 2018), mobile short video platforms (Ren et al., 2021), and gaming apps (Chen et al., 2018). However, the context of mobile payment apps remains largely overlooked. Despite mobile app stickiness being a critical goal in the app market, its role and significance in the mobile payment app domain are underexplored (Martinez & McAndrews, 2021).

Quality dimensions influencing customer experience

Delone and McLean’s Information Systems Success Model (ISSM) encompasses three key quality constructs namely system quality, information quality, and service quality which have been instrumental in understanding the factors that drive mobile payment usage and satisfaction (DeLone & McLean, 2003). This model is widely used in various contexts that examine pre-adoption and adoption contexts (Mouakket, 2020; Sfenrianto & Vivensius, 2020).

Recent studies have utilized the ISSM to examine the antecedents of mobile payment stickiness, including trust, perceived usability, and service quality. Stickiness, defined as the degree to which consumers are willing to continue using a mobile app, is a key outcome that is influenced by both hedonic and utilitarian factors (Yoo, 2016). Abu Farha et al. (2024) further elaborate on the emotional and utilitarian values that underpin app respect and app love, which subsequently lead to app evangelism and defence, strengthening the relationship between consumers and digital platforms. Alksasbeh et al. (2019) proposed an extended ISSM model for mobile social network applications in learning environments, highlighting the importance of system, service, and information quality in driving user satisfaction.

These studies show that the ISS model has been widely adopted and investigated in various consumer behaviour (Pushparaj et al., 2022). However, little research has been conducted on the ISS model and stickiness, as well as on user satisfaction with m-payment app experiences. Consequently, an investigation of the following relationships is required. To supplement the literature, we have additionally examined about 27 manuscripts relating to mobile payment technologies (Appendix 2) and discovered the research gap and objective to be still relevant.

Theoretical model and hypotheses development

The conceptual model for the present study is derived from the ISSM literature (Figure 1). The ISSM model has three quality constructs, namely, system, information, and service, adopted as independent variables. Satisfaction with the experience and stickiness are the two dependent variables. The original ISS model studied satisfaction and usage. It ignored customers’ cognitive experience. Hence, we are adapting the level of satisfaction with the customer experience and stickiness, which depicts the post-adoption behaviour well. The rationale for choosing the three independent quality variables is drawn from previous studies examining the impact of website quality on various behavioural outcomes (Gao & Li, 2019; Tsao et al., 2016). Since the transactions made through payment apps are like those made through websites, consumers have a higher chance of experiencing the three qualities that could predict their stickiness and overall satisfaction with their experience with the app. In addition, (Motiwalla et al., 2019), have mentioned that the ISS model would be the best suited for studying the post-adoption behaviour of m-payment apps.

Figure 1.

Proposed research model.

Information quality

Delone and Mclean (2003) proposed that information quality refers to personalized, complete, relevant, easy to understand, and secure content provided by websites, which facilitates online transactions. Information quality is the customers’ basic expectation (Yu et al., 2015). Similar to websites, mobile payment apps are expected to provide complete, relevant, and accurate information (Mouakket, 2020) in serving customers. Specifically, mobile payment apps allow consumers to check static and dynamic financial, transactional, historical, promotional, and purchase details. Accurate, relevant, and useful information builds a positive experience for apps. M-payment apps must provide information relevant to consumer needs, such as transaction history, account balance, transaction status, and due dates. Information quality meets customers’ specific requirements, resulting in a positive and satisfying customer experience. Similarly, customers are expected to revert or stick to the same application for additional information or repeated information when the provided information is of high quality since it reduces operational difficulty (Sharma & Sharma, 2019). For example, there are scenarios in which app users check their bank balance from payment apps and recheck from the same app after a transaction to ensure correctness and reconfirmation. Furthermore, quality information tends to save time and meet app users’ expectations (Teo et al., 2015), therefore, by providing economic value, consumers would prefer to stick to apps that provide accurate information. Likewise, consumers more logically would prefer to stick with mobile apps that offer consistent benefits in terms of accurate and credible information Consequently, the following hypotheses were formulated:

H1A: Information quality of m-payment apps has a positive influence on the customer’s satisfaction with their experience.

H1B: Information quality has a positive influence on m-payment app stickiness.

System quality

System quality measures websites’ usefulness, usability, responsiveness, reliability, and flexibility (DeLone & McLean, 2004). M-payment apps have comparable features wherein users can navigate numerous application functionalities at any time and from any location (Jenneboer et al., 2022). Users of payment applications prefer prioritized system quality and seamless transactions. Furthermore, good system quality provides reliable and up to date information in a format that is easy to access and share with others as required by users. In addition, reliability refers to the performance of apps with minimal technical bugs and glitches, whereas flexibility focuses on how the apps promptly adapt to users’ needs (Nguyen et al., 2022). Subsequently, all such factors contribute to overall consumer satisfaction with a positive experience. Users of m-payment apps typically go through several processes for their transactions. A substandard system quality may undermine or impede customers’ overall payment experience (Zhou, 2013). The primary role of a quality system is to enable customers to complete such transactions with ease by reducing any risk associated with the transaction (Upadhyay et al., 2022). Furthermore, system quality also keeps users sticking to certain apps by enhancing the perceived transactional convenience, which in turn reduces consumer effort expectancy and enhances performance expectancy towards the app (Teo et al., 2015). Like websites, payment applications with highly engaging user interfaces will entice users to spend more time in apps, hence establishing a strong habit towards the app thereby cultivating stickiness (Giri & Vats, 2019). Payment applications with a well designed user interface create a mental map in their users’ brains that is activated when they need to carry out any additional payment transaction, which encourages users to stick with a certain app for future transactions (Xu & Schrier, 2019).

Therefore, it was hypothesized that:

H2A: System quality of m-payment apps has a positive influence on the customer’s satisfaction with their experience.

H2B: System quality has a positive influence on m-payment app stickiness.

Service quality

Delone and Mclean identify service quality as an important dimension in an online environment, where customers assess service providers’ overall performance. Service quality over here is described as the service provider’s comprehensive support for users at all stages with responsiveness, security, empathy, and aftercare (DeLone & McLean, 2004). Service quality is a key factor in measuring the overall success of an information system (Geebren et al., 2021; Wani et al., 2017), and it is referred to as the “degree of excellence of service performance.”Yu et al. (2015) stated that a website’s service quality is important in meeting customers’ expectations. As a part of improving the service quality of m-payment apps, chatbots with AI technology are introduced to solve customers’ needs in real time (Chauhan et al., 2022). Nevertheless, the real time interaction effect of AI chatbots and customer service teams compensates for the absence of humane involvement in service delivery. The customer service team behind the payment apps when focuses and puts an emphasis on responding to queries quickly, offering workarounds, and following up until issues are fixed. It would always cultivate a positive experience towards the payment app as this satisfies and fulfils customers’ banking needs. Moreover, such behaviours reinforce consumers’ positive perceptions and encourage them to stick to these apps (Montazemi & Qahri-Saremi, 2015), overall good services make customers get attached to the apps’ operation, functionality, and know how, eventually, it becomes a switching barrier and would make them stick to apps to save time and cognitive resources (Lien et al., 2017).

Thus, the following hypotheses were proposed:

H3A: Service quality of m-payment apps has a positive influence on the customer’s satisfaction with their experience.

H3B: Service quality has a positive influence on m-payment app stickiness.

M-payment app stickiness

Stickiness refers to a user’s propensity for repeat usage and extended use of a certain app. Stickiness as such improves customers’ intention to reuse or revisit a particular website as they tend to depend on it. Studies revealed that customers develop stickiness toward certain websites or products when they are highly satisfied towards their features (Polites et al., 2012). Most of the satisfied customers tend to evaluate a particular website positively and stick to it to avoid post-purchase regret since it saves time and effort to choose a new one (J. (Jeanne) Kim et al., 2021). In our case, customer experience has varying degrees of influence on customer satisfaction, which in turn affects stickiness (Bao & Zhu, 2022). Customers who are exposed to the design elements of mobile payment applications develop a habit of using them, especially when they are satisfied with their usage experience, eventually, it leads to sticking to it for all types of transactions.

Thus, the following hypotheses are proposed:

H4: Customer satisfaction with the experience positively influences the app’s stickiness towards m-payment apps.

Mediating role of customer experience satisfaction

The study also aims to explore that a satisfying customer experience is a key mediator in the relationship between quality dimensions (information, system, and service) and app stickiness within mobile payment contexts. High information quality, focusing on accuracy and timeliness, reassures users with reliable transaction data, further boosting satisfaction (Ryu & Ko, 2020). System quality, which encompasses functionality and usability, enables seamless and user friendly interactions that are vital for continued app usage (Sharma & Sharma, 2019). Similarly, Service quality, marked by reliability and responsiveness, strengthens user confidence and satisfaction with the app (Lien et al., 2017). Collectively, these quality dimensions contribute to a satisfying experience that aligns with users’ expectations, thus promoting customer stickiness (Alksasbeh et al., 2019).

Research on digital platforms underscores that quality driven satisfaction enhances loyalty and reuse intentions, which is equally relevant in mobile app contexts (Wani et al., 2017). Satisfied users are more inclined to engage repeatedly with apps that deliver reliable information and intuitive interfaces. Moreover, when digital quality fosters an immersive experience, it sustains long term engagement and loyalty (Ali, 2016). Based on these insights, we propose the following hypotheses to explore satisfaction’s mediating effect in mobile payment app stickiness:

H1C: Customer experience satisfaction mediates the relationship between information quality and mobile app stickiness.

H2C: Customer experience satisfaction mediates the relationship between system quality and mobile app stickiness.

H3C: Customer experience satisfaction mediates the relationship between service quality and mobile app stickiness.

The following research model is proposed based on the hypotheses developed.

Research methodology

Measurement development

To achieve the objectives of this study, the survey instrument was designed and validated using well established literature. Each construct consisted of multiple items measured on a seven point Likert scale, ranging from strongly disagree (1) to strongly agree (7). To ensure content validity, existing and widely used items were adapted for this study’s context. Specifically, Information Quality, System Quality, and Service Quality items were drawn from (Tam & Oliveira, 2016), while items for stickiness and customer experience satisfaction were adapted from Agarwal and Karahanna (2000), Yoo (2016), and Mclean et al. (2018), respectively. Appendix 1 shows the items for this study and the supporting literature for each construct.

The survey questionnaire had two parts, comprising respondents’ demographic details and questions regarding the constructs mentioned above. The instrument was reviewed by linguistic experts, academic experts, and industry experts to clarify the aforementioned items of the constructs (Groves, 1987).

Data collection

The survey targeted Indian users of mobile payment applications who had been actively using the apps for over three months and had conducted at least one transaction in the previous week. An online based survey was carried out through Survey Monkey, using convenience and snowball sampling techniques, chosen for their alignment with the study’s goals, focusing on their most recent experiences.

The sample size was determined using the G-power analysis, and it was found that 129 responses are sufficient to explain the proposed model. Initially, 328 responses were gathered. Subsequently, the data was carefully refined by addressing incomplete responses, managing missing data, and eliminating outliers. The final dataset encompassed 280 individuals. Also, the adequacy of the sample was evaluated using the Kaiser Meyer Olkin (KMO) measure, which yielded a value of 0.884 and Bartlett’s test of sphericity (p < .05), confirming suitability for analysis.

Data analysis

The study employed the partial least squares (PLS) regression method within a structural equation modelling (SEM) framework to analyse the data. This method was selected for its effectiveness in exploratory research and its suitability for smaller sample sizes (Fornell & Bookstein, 1982). Furthermore, the PLS algorithm provides benefits such as flexibility in handling data normality constraints and ensures robustness, as the sample size met the criterion of being at least 10 times the maximum number of inner model paths directed toward any construct.

Sample profile

The demographic profile of the respondents demonstrates a strong inclination toward mobile payment app usage, aligning with the characteristics of the target population. Research consistently shows that young individuals are early adopters of new technologies, particularly in digital financial services (Koenig-Lewis et al., 2015). In Asian countries, younger demographics exhibit a greater propensity to engage with internet based technologies compared to older generations (Pandey & Chawla, 2019). With 82.8% of the respondents aged between 18 and 35 years, the sample represents a demographic segment most likely to adopt and use mobile payment apps.

Moreover, the high educational attainment of respondents 93% holding undergraduate or postgraduate qualifications—further supports their predisposition to use mobile payment technologies. Studies indicate that educated individuals are more likely to embrace digital innovations due to greater awareness of their benefits and ease of navigation. The geographical distribution of respondents, encompassing urban (49.6%), semi-urban (28.5%), and rural areas (21.7%), reflects diverse living environments, yet these groups share access to mobile technologies, enhancing the relevance of the study. Urban and semi-urban respondents, in particular, are known for their higher adoption rates of digital services due to better infrastructure and connectivity.

These characteristics confirm that the sample not only aligns with the broader population of mobile payment app users but is also inherently inclined to adopt and actively use such technologies, making it an appropriate representation for this study (Table 1).

Table 1.

Demographic Details.

Demographics	Category	Frequency (n = 280)	Percent
Gender	Male	199	71.0
Gender	Female	81	28.9
Age (years)	18–25	121	43.2
	26–35	111	39.6
	36–45	38	13.5
	46–55	9	3.2
	Above 55	1	0.35
Educational Qualification	School	10	3.57
	Diploma	4	1.42
	Undergraduate	106	37.8
	Postgraduate	127	45.3
	Doctorate and above	28	10.0
	Others	5	1.78
Place of living	Urban	139	49.6
	Semi-urban	80	28.5
	Rural	61	21.7

Results

The obtained sample data were statistically analysed using SPSS 23.0. The Partial Least Squares technique with Structural Equation Modelling (PLS-SEM) was employed using the SmartPLS 3.0 software to test the proposed hypotheses. The data analysis follows a two stage SEM approach. The first stage evaluated the measurement model, and the second stage evaluated the structural equation model.

Measurement model evaluation

Reliability and construct validity were achieved through Confirmatory Factor Analysis (CFA) for evaluating the measurement model. The following CFA assumptions must be met to satisfy the measurement model’s convergence validity criterion. Individual items in the construct should be above the standardized loading value of 0.5; Or each construct should be above the standardized loading value of 0.7 (Hair Jr. et al., 2023); composite reliability (CR) values should be more than 0.70, and average variance extracted (AVE) values should be above 0.50 (Kline, 2015).

Common method bias (CMB) is a common problem in self reported quantitative studies (Spector, 2006), which undermines the validity of the measurement (MacKenzie & Podsakoff, 2012). To address the issue of common method bias, we adopted three techniques. First, Harman’s single factor test was carried out, and the corresponding result shows that the most variance explained by the single factor is 35.685%, less than 50%, and it does not account for most of the variance. Second, we ran a full collinearity test, which was measured through a random dependent Variable, adopted from the literature (Kock, 2015), the results showed that all the factor levels or inner VIFs were lower than three, indicating no issue of common method bias (Kock, 2015). Finally, we also verified CMB through the unmeasured latent method (ULM) and found no significant difference in the R square value between both with marker variable and without marker variable.

As shown in Table 2, factor loadings of all items were above 0.70, and Cronbach’s alpha values were more than 0.70 for the factors, which indicates that the measurement model was reliable. Composite reliability and AVE values were above the desired threshold values. Hence, the collected sample set had good convergent validity, and all constructs had adequate internal consistency.

Table 2.

Measurement model Validation—Construct Reliability and Validity.

Variable	Items	CFA loading	Average loading	α	CR	AVE
Information quality	IQ1	0.850	0.856	.818	.892	.734
	IQ2	0.901
	IQ3	0.817
Customer experience satisfaction	CE1	0.890	0.880	.855	.911	.774
	CE2	0.897
	CE3	0.852
M-payment app stickiness	STK1	0.909	0.912	.899	.937	.832
	STK2	0.935
	STK3	0.894
Service quality	SERQ1	0.878	0.899	.882	.926	.807
	SERQ2	0.907
	SERQ3	0.911
System quality	SYSQ1	0.847	0.884	.861	.915	.782
	SYSQ2	0.904
	SYSQ3	0.900

Discriminant validity measures a construct to differentiate its items from other constructs. It is estimated by checking the correlation values from the correlation matrix, which should be higher than 0.70. AVE’s square root (bold diagonal values) for each construct should be higher than the correlation values (non bold values) with other constructs (Fornell & Larcker, 1981; Hair Jr. et al., 2023). Furthermore, the Heterotrait Monotrait (HTMT) ratio is more robust than the Fornell Larcker criteria, with a criterion rate of 0.85 for the HTMT ratio correlation scores among constructs, demonstrating discriminant validity. All constructs of this study had a correlation value of less than .85. As a result, the model exhibited discriminant validity (Hair Jr. et al., 2023). The discriminant validity for the model using the Fornell Larcker criterion and the HTMT ratio is given in Table 3. The measurement model is shown in Figure 2.

Table 3.

Discriminant Validity for the Model Using the Fornell-Larcker Criterion and Heterotrait-Monotrait Ratio (HTMT).

Fornell-Larcker Criterion
Variable	Information quality	M-payment CES	M-payment app STK	Service quality	System quality
Information Quality	0.857
M-payment CES	0.506	0.880
M-payment app STK	0.490	0.658	0.912
Service Quality	0.334	0.376	0.226	0.899
System Quality	0.711	0.475	0.531	0.344	0.884
Heterotrait-Monotrait Ratio (HTMT)
Information Quality
M-payment CES	0.601
M-payment app STK	0.563	0.743
Service Quality	0.394	0.425	0.247
System Quality	0.851	0.546	0.600	0.389

Note. CES = customer experience satisfaction; STK = Stickiness; Square root of AVEs in Bold- diagonal values.

Figure 2.

Research model with path coefficients.

Structural model evaluation

Following the validation of the measurement model, the hypotheses were empirically tested using the dataset and analysed with SmartPLS 4.0. The structural model evaluation involved examining path coefficients, t-values, the R² value, and the SRMR value, which was used to assess the model’s goodness of fit. The SRMR value of 0.056 indicates an acceptable fit, aligning with recommended thresholds. Table 4 presents the results, including standardized path coefficients and t-values, while the R² value demonstrates the model’s explanatory power (Thakur, 2014).

Table 4.

PLS-SEM Path Coefficients of Structural Model (direct effect).

Hypothesis	Relationship	Std. Path coefficients	T Statistics	p Values	Conclusion
H1A	Information Quality -> CES*	0.302	2.9377	.004**	Supported
H2A	System Quality -> CES	0.188	1.685	.092	Not supported
H3A	Service Quality -> CES	0.211	3.770	.000***	Supported
H1B	Information Quality -> app STK*	0.059	0.695	.472	Not Supported
H2B	System Quality -> app STK	0.264	3.255	.001**	Supported
H3B	Service Quality -> app STK	-0.086	1.426	.167	Not supported
H4	CES -> M-payment app STK	0.535	8.326	.000***	Supported

CES = M-payment app customer experience satisfaction, app STK = M-payment app Stickiness. Bold indicates to differentiate the statistically significant hypotheses.

***

<0.001; **<0.01.

Bootstrapping techniques with 2,500 subsamples were utilized to test the significance of all paths in the proposed model. The results, as presented in Tables 4 and 5, reveal that information quality (β = .302, t = 2.937) and service quality (β = .211, t = 3.770) significantly influenced customer experience. However, system quality (β = .188, t = 1.685) was not found to have a significant effect on customer experience.

Table 5.

PLS-SEM Specific Indirect Effect.

Hypothesis	Relationship	Std. path Coefficients	T Statistics	p Values	Effect
H1C	Information Quality -> CES -> M-payment app Stickiness	0.162	2.657	.008	Full Mediation
H2C	System Quality -> CES -> M-payment app Stickiness	0.100	1.745	.081	No Mediation
H3C	Service Quality -> CES -> M-payment app Stickiness	0.113	3.254	.001	Full Mediation

CES = M-payment app customer experience satisfaction.

Further examination of the effects on m-payment app stickiness showed that information quality (β = .059, t = 0.695) and service quality (β = −.086, t = 1.426) did not significantly impact app stickiness. In contrast, both system quality (β = .264, t = 3.255) and customer experience satisfaction (β = .535, t = 8.326) demonstrated a significant and substantial influence on app stickiness. These findings support the acceptance of hypotheses H1A, H3A, H2B, and H4, while hypotheses H1B, H2A, and H3B are rejected.

To explore the potential mediating role of customer experience satisfaction, bootstrapping analysis was conducted. The results indicated that customer experience satisfaction significantly mediates the relationships between information quality (β = .162, t = 2.657) and m-payment app stickiness, as well as between service quality (β = .113, t = 3.254) and app stickiness. Consequently, H1C and H3C are accepted, whereas H2C is rejected.

Predictive relevance of our model

The explanatory power of the independent variables on the dependent variables was studied with the help of R² values. The results showed that R² values for m-payment app satisfaction with experience and stickiness were 0.321 and 0.501, respectively. It means that all quality constructs jointly explained 32.1% variance by customer experience satisfaction and 50.1% variance by m-payment app stickiness. The R² value of the dependent variables showed that the model had substantial explanatory power. The predictive power of the R² value explained the in-sample explanatory power better than the out-of-sample explanatory power (Hair et al., 2019). The study reports the predictive accuracy of the proposed model using the Q² value. If the value of Q² is above zero, the predictive relevance of exogenous variables has a meaningful prediction of the endogenous variables. A blindfolding procedure was used to obtain the Q² value of customer satisfaction with the experience and m-payment app stickiness, which was 0.234 and 0.400, respectively. The results indicated that the path model’s predictive relevance was adequate (Hair Jr. et al., 2023).

Discussion and conclusion

The present study investigates how quality dimensions—specifically information quality, system quality, and service quality— influence customer satisfaction with mobile payment (m-payment) app experiences and stickiness, while examining the mediating role of customer experience satisfaction (CES). These quality dimensions were evaluated through multiple hypotheses to determine their effect on user satisfaction, loyalty, and continued app usage.

The results indicate that information quality plays a significant role in shaping customer satisfaction with their experience (H1A). Information quality refers to the accuracy, relevance, and completeness of information, such as balance details, transaction history, and payment reminders. This study found that providing reliable information is crucial for users making financial decisions, leading to higher level of satisfaction. Similar findings were highlighted by Nguyen et al. (2024), who emphasized the importance of quality information in enhancing user experiences on digital platforms. Islam et al. (2024) also supported this, noting that features like accurate payment histories and reminders significantly contribute to user satisfaction.

The study also revealed that system quality had no significant impact on customer satisfaction (H2A). System quality, encompassing reliability, functionality, and usability, plays a pivotal role in shaping the user experience. However, the challenges associated with m-payment apps in India—such as transactional delays and banking integration issues—hinder users from fully experiencing satisfaction (Sharma et al., 2024). Despite these challenges, system quality positively affected app stickiness (H2B), indicating that users continued using m-payment apps for the convenience they offered over traditional banking, such as faster balance checks and bill payments. Owusu-Agyemang et al. (2024) similarly noted that despite technical issues, mobile payment adoption continues due to increased convenience and efficiency.

Service Quality was found to significantly influence customer experience satisfaction (H3A). Service quality encompasses reliability, responsiveness, and empathy from the service provider. The study found that effective customer support greatly enhances user satisfaction, which aligns with Rahman et al. (2024), who highlighted the positive impact of quality customer service in both financial technology and mobile banking contexts. Interestingly, service quality did not exhibit a direct influence on app stickiness, contrary to initial hypotheses (H3B, H3C). This may be explained by the emphasis on system performance in digital platforms, where users prioritize smooth transactions over customer support interactions (Nguyen et al., 2024).

Interestingly, information quality did not significantly impact app stickiness (H1B). This may be because stickiness is more influenced by system utilization, app duration, and user interaction rather than purely informational content, as highlighted by Bai et al. (2024).Sharma et al. (2024) demonstrated that factors such as flow and interactivity are crucial in promoting app stickiness. Additionally, Shao et al. (2020) suggested that new users might be more attracted to visual and interactive features, while experienced users tend to focus more on the informational content, indicating a shift in user priorities over time.

Customer experience satisfaction (CES) significantly mediated the relationship between quality dimensions and app stickiness. The findings suggest that CES has a direct impact on app stickiness, implying that customers who have positive experiences are more likely to continue using the app. This aligns with findings from Matemba et al. (2018), who observed that satisfaction with system functionality directly enhances app stickiness (Sharma et al., 2024).

In summary, the study demonstrates that while quality dimensions like information quality and service quality significantly influence user satisfaction, their direct effects on app stickiness are varied. Only system quality showed a direct impact on app stickiness, highlighting the importance of addressing system related issues to retain users. These findings emphasize the complexity of factors contributing to user loyalty and continued use of m-payment applications. The importance of system quality for stickiness suggests that developers should focus on improving the reliability and usability of m-payment apps to enhance long term user engagement.

Implications of the study

Both theoretical and managerial implications of the study have been discussed below.

Theoretical implications

The theoretical framework of this study is built upon the Information Systems Success (ISS) model to ensure that the selected variables comprehensively and appropriately explain the phenomenon under investigation—namely, user satisfaction with the experience and stickiness in mobile payment applications. The ISS model was chosen because it aligns with the study’s objective of examining how quality dimensions—system quality, information quality, and service quality—impact user experience and long-term app usage. Unlike traditional models such as TAM and UTAUT, which primarily focus on technological factors like ease of use and perceived usefulness, the ISS model provides a more nuanced approach by emphasizing app quality dimensions that directly shape user satisfaction and engagement.

This research is necessary as it addresses a critical gap in post-adoption behaviour studies, which have historically focused on loyalty, continuance intention, and retention (Liébana-Cabanillas et al., 2020; Ryu & Ko, 2020) but have overlooked the role of satisfaction with the user experience in driving app stickiness. By leveraging the ISS model, the study contributes to the theoretical understanding of how consumers evaluate their m-payment experience based on the quality and service dimensions of the app.

The findings theoretically provide a new understanding of how consumers evaluate their experience. Both the quality and service dimensions of the ISS model predict customer satisfaction with the mobile payment experience. This suggests that the content and accuracy of the information provided by the app, as well as the quality of the services offered, play significant roles in shaping the overall consumer experience. Furthermore, the study highlights the importance of the objective of using m-payment applications, such as conducting financial transactions, accessing flexible services, and saving time. These objectives were found to have a greater impact on consumer experience compared to the functionality of the app itself. This suggests that users prioritize the outcome and benefits they can achieve through the app over its technical features.

Another theoretical contribution of the study is the mediating effect of satisfied user experience on app stickiness. The findings suggest that when users have a positive and satisfying experience with m-payment applications, they are more likely to develop stickiness towards those apps, meaning they are more inclined to continue using them and remain engaged over time. Interestingly, the study also identifies system quality as a significant influencer of app stickiness. This indicates that the performance and functionality of the app, as well as the absence of technical bugs and glitches, play a crucial role in retaining users and encouraging them to stick to the app.

However, the theoretical limitation lies in the exclusion of external or emerging factors outside the scope of the ISS model that may also influence user satisfaction and stickiness. For example, personal innovativeness, social influences, or contextual factors like economic or cultural differences are not explicitly considered within the ISS framework. While this focused approach allows for a deeper exploration of quality dimensions, it also narrows the scope of the analysis, potentially leaving out other relevant variables.

Managerial implications

The current study provides us with the following practical implications. First, the present study improves our understanding of quality dimensions that significantly enhance the customer experience. However, the usage of such apps is still not in full. So, enhancing app quality and increasing awareness of their benefits, such as ease of use, time savings, and reliability (easy to use, time-saving, convenient, and reliable) is likely to encourage broader adoption of such applications.

Second, enhancing information quality and system quality can increase user retention. The quality of information, including accuracy and timeliness, and system reliability directly influence whether users continue using an app or switch to a competitor (Sharma et al., 2024). Mobile marketers and decision makers should prioritize these quality dimensions to build a loyal user base.

Lastly, app developers should add features that increase engagement. Considering the highly competitive nature of the mobile payment market, integrating features such as chatbots to address common queries can enhance user interaction and engagement. These features not only address user issues effectively but also encourage positive word-of-mouth, enhancing both app stickiness and satisfaction. Incorporating user feedback to redesign and personalize payment experiences further strengthens loyalty and long-term engagement. Responding effectively to user expectations is key to fostering user loyalty (Sharma et al. 2024). In conclusion, focusing on improving app quality, raising awareness of app benefits, and delivering personalized, engaging experiences are crucial strategies to boost adoption, retention, and satisfaction in the m-payment landscape.

Limitations and future directions

The aim of this research was to investigate the impact of the three quality constructs of an m-payment app on customers’ experience and stickiness. Future researchers might augment our model with constructs from different contexts, such as hedonic and utilitarian aspects (Salimon et al., 2017), as well as certain technology related characteristics like speed and aesthetics (Wang et al., 2010), with the goal of better understanding the impact on customer experience.

Given that this study employed non-probability sampling methods. A sample with a wider range of responses will bring more contributions to the body of knowledge. In the current study, system quality does not imply the impact of the demographic pattern of the sample population. Studying the same model with a different sample set country wise will provide more insights. At last, future research can be carried out with different mediators, and their role in the m-payment app customer experience can be examined.

To summarize, the use of mobile payment apps has recently risen, becoming a natural means of financial transactions. In the research on mobile payment apps, there is no clear holistic theoretical framework. Furthermore, the flexibility and relevance of the ISS model make it suitable for searching for the impact of quality dimensions on users, as our findings show users in India consider information and service quality dimensions to be the most important factors of their positive experiences, and system quality is what keeps them using their existing apps. To keep more users loyal, app makers should focus on providing well designed, faster, easier, content rich, glitch free, and smooth transactions to their customers.

Footnotes

This is to acknowledge that a significant portion of the results presented in this manuscript is derived from Nancyprabha Pushparaj’s doctoral thesis. The insights and findings from the thesis have meaningfully contributed to the development and articulation of the current research.

ORCID iDs

Nancyprabha Pushparaj

Manoraj Natarajan

Funding

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

Declaration of conflicting interests

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

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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