Analysing the effects of satisfaction on behavioural intentions about M-wallet adoption: a study on Generation Y

New innovation, technologies and digitalization of life are shaping the ways of doing business. The rise of the digital era has caused the internet and technology to dominate daily living. Consumer behaviour has changed from conventional to digital, thanks to cellular technology. Mobile phone use has completely changed the Indian market’s landscape. Previously, having a landline was seen as a luxury relished by the wealthy and a prestige symbol (Campbell and Singh, 2017). Smartphone-powered technology advancements in mobile communication have caused a paradigm change in the field of communication. In a nation like India, smartphones have permanently changed the landscape. This has caused a drive for development in various areas of trade, business, the education sector and the hospitality sector, among others.

According to reports, there are a larger number of mobile phones customers than people with a bank account at the global level. With the increase in demand and usage of smartphones, the behaviour of consumers has undergone a drastic change. Among the most significant innovations is the emergence of mobile wallets. Consumers all around the world nowadays are using smartphones for doing business or to make payments and various other things (Hasan and Gupta, 2020). This clearly illustrates the overarching importance of these devices. In this busy world, everyone prefers a technology which is fast, convenient, time-saving and provides multiple services on a single platform (Alaeddin et al., 2018). In this regard, mobile payment services promote an advanced multiplier technique (Singh et al., 2019). According to a KPMG report, a boom in the use of mobile payment systems has resulted from the mobile payment revolution and its changing forms (Saxena and Tripathi, 2021).

Mobile payment service means any financial services rendered through a mobile phone (Husainah et al., 2022). Pousttchi (2008) defined mobile payment as a system which highlights the initiation, authorization or completion process of payment via mobile communication techniques and devices. The RBI has defined mobile payments, “as information exchanged between a bank and its customers for financial transaction through the use of mobile phones (RBI). A mobile wallet is one of the forms of mobile payment systems. A mobile wallet is a way to carry cash in digital format. Mobile wallets are a type of electronic wallet which is used to perform various financial services via a mobile phone. It is a digital software program that is stored in consumer’s mobile devices and used to make payments. The Reserve Bank of India has designated M-wallets as prepaid instruments (PPI). In India, there are primarily three kinds of mobile wallets: closed PPI, semi-closed PPI and open PPI wallets (Chawla and Joshi, 2019) (see Table 1). Closed wallet user can use the funds stored to make transactions only with the issuer of wallet, such as Amazon Pay. Semi-closed wallets allow users to make transactions at listed merchants and locations, such as Paytm and Google Pay. Open wallet users can avail facilities of semi-closed wallets, and in addition to this, they are the only wallets through which we can withdraw cash, such as ATMs, debit cards and credit cards (refer to Table 1). As cashless transactions become increasingly common mobile reshaping consumer behaviour. Moreover, government initiatives promoting digital financial inclusion and the rise of fintech firms have also accelerated the adoption of mobile wallets in India.

In 2004, oxygen was the first-ever E-wallet launched in India by an alumnus of IIT Roorkee (Krishnakumar). It is the first non-bank wallet. M-wallets have gained popularity because of their convenience, rewards, contactless payment, speed and ease of use (Shaw, 2014). The drastic change came all around the world with the arrival of the 2019 pandemic, people avoided physical cash and moved towards a cashless economy. Even the World Health Organization (2020) has suggested that individuals should consider using cashless transactions as a way to avoid tainted currency notes (Mastor, 2021). Demonetization and COVID-19 were major factors in the expansion of the digital payment system in India (Singh et al., 2019).

The current study has used the technology acceptance model (TAM) model for developing the proposed model of the study. The authors have aimed to bridge the gap by investing in the factor of satisfaction leading to behavioural intention. This study was conducted only on Generation Y.

This study is divided into five parts. The first part comprises the introduction. The second part includes a review of the literature and theoretical foundation. The third part explains research methods, data collection methods and techniques. The fourth part provides the result of the empirical test. The fifth part presents the conclusion, limitation and future scope of the study.

According to Gawas (2022), 68% of people in India do their financial transactions via online or mobile banking. The Asia Pacific region has the highest number of mobile wallet users (Malyshev, 2023). Retail and e-commerce are the largest segments, with a global share of 30% (Malyshev, 2023). As of now, in India, there are 217 m users of mobile wallets, which is expected to rise up to 434 m by 2025 (TechSci Research). According to the economic survey 2022–2023, India has a public adoption rate for fintech that is 87% higher than the global average of 64% (Government of India, 2023). The global market is projected to be USD12 trillion by 2030, indicating a seismic shift in consumer payment preferences (Ekre and Sumant, 2023).

RBI released a composite DPI (digital payment index) for the year 2024. In September 2024, the DPI was 465.33 (RBI, 2023). To gauge the degree of payment digitization in India, the RBI introduced DPI. It was built using March 2018 as the starting month. Payment enablers (25%), payment infrastructure demand side (10%), payment infrastructure supply side (15%), payment performance (45%) and customer centricity (5%) are the five characteristics that make up the DPI. The DPI is published by RBI on a semi-annual basis (Dristhi, 2021).

According to the RBI’s payment vision 2025, the main focus for 2025 is e-payment for everyone, everywhere and all the time. Integrity, inclusivity, innovation, institutionalization and internationalization are the five objectives of the payment vision.

Researchers have widely used TAM (technology acceptance model) used to predict the behavioural intention of consumers towards new technology (Chawla and Joshi, 2019; Ma and Liu, 2004; Singh et al., 2019). Many studies have agreed that TAM is the most suitable model to understand user’s acceptance towards technology (Kavitha and Kannan, 2020). TAM was developed by Fred Davis in 1989 on the basis of the theory of reasoned action (Azjen, 1991). TAM is a model which indicates how users adopt new technology Herdioko et al. (2021) and Davis (1989) made an attempt to apply psychological factor into information system and consumer adoption. The main two construct of TAM model are perceived usefulness (PU) and perceived ease of use (PEOU), as it is assumed that PU and PEOU have a major influence on individuals attitude formation towards new technology acceptance (Shin, 2009; Herdioko et al., 2021; Ma and Liu, 2004). It is considered the most suitable for the decision to accept new technology (Herdioko et al., 2021).

The current study is based on the TAM model. The model has been applied to study the effect of variables on behavioural intention. A mediating variable, namely satisfaction, has been added to analyse the direct effect of perceived usefulness, perceived ease of use and perceived security and further its effect on behavioural intention. The authors have taken PU, PEOU and PS as they are considered the most effective variable which effect to BI of individuals (Jayantri et al., 2021; Kavitha and Kannan, 2020; Chawla and Joshi, 2019; Madan and Yadav, 2016). Further mediating variable satisfaction is added, as there is a dearth of work on this relation between perceived usefulness, perceived ease of use and perceived security and satisfaction (refer to Table 2).

1. Perceived Usefulness (PU)

According to Davis (1989), PU is the degree to which individuals trust that adopting a certain system will improve their work performance. Aydin and Buranz (2016) said that if people feel mobile wallets provide superior benefits than other payments, then is assumed to be useful, but the major barrier in constructing positive perceived usefulness was lack of clear understanding of technology (Kavitha and Kannan, 2020). Further, the study done by Yang et al. (2021) said that PU had a positive effect on intention among more in adults; Kavita and Kannan (2020) showed a positive relation between PU and PR. The results also showed that consumers felt that digital wallets were very useful in a pandemic, as they could shop easily and had contactless purchasing and Ladkoom and Thanasopon (2020) concluded that M-wallets were considered useful, as they helps people to make payment at very minimal cost with no effort. Moreover, study done by Gupta et al. (2023) in Malaysia indicated that perceived usefulness was the most significant factor which motivated people to shift from physical cash to digital payment. Additionally, the results of the study done by Kavita and Kannan (2020), Aydin and Burnaz (2016), Bagla and Sancheti (2018), Shin (2009), Singh et al. (2019) and Chawla and Joshi (2019) were the same, where they stated that PU has a positive impact on the attitude of consumers. Generation Y of Indonesia felt that M-wallets were useful in making payments (Pertiwi et al., 2020). Whereas, in Bangladesh, PU was considered as the major factor which influenced the younger generation to use technology. Karim et al. (2022) and Putra and Salim (2023), reported that PU had a positive impact on BI due to their flexibility, discounts and coupons, cash back and rewards; PU was also the main predicator for trust (Amin et al., 2014). But a few studies also indicated that PU was insignificant, as people did not want to use m-wallets on a regular basis in the future and PU also had no impact on BI and attitude (Chawla and Joshi, 2020). Lastly, the authors also suggested that if service providers have to enhance PU, then these mobile applications should be more user-friendly and easily understandable.

1. Perceived ease of use (PEOU)

PEOU is defined as the extent to which individuals believe that using a particular system will be free from physical and mental efforts (Davis, 1989). Past studies revealed that PEOU and PU are the main indicators of the formation of attitudes towards technology (Singh, 2019; Aydin and Burnaz, 2016). In other words, we can also say that PEOU is the extent to which a particular system will be free from efforts (Herdioko et al., 2021; Kavita and Kannan, 2020) and user-friendly (Kavita and Kannan, 2020). The findings of the various studies indicated that, according to Chawla and Joshi (2019), if a system is easy to use, it would automatically enhance its usefulness and satisfaction among users. Shin (2009), in their study, found that PEOU was the most important factor in the older generation. The results were also the same as those of studies done by Yang et al. (2021) and Chawla and Joshi (2019) and Campbell and Singh (2017), which considered PEOU as the main indication towards influencing BI. Furthermore, PEOU is also the main construct, which explains the covariance between PU and PEOU. But in a few studies, the effect of PEOU was insignificant (Herdioko et al., 2021; Campbell and Singh, 2017) on intention.

1. Perceived security (PS)

Perceived security is defined as the degree to which a consumer believes that using a particular mobile payment procedure secure (Shin, 2009). Security is the main concern among people while using M-wallets because people are afraid of scams and losing their personal credentials. In the study by Herdioko et al. (2021), the results depicted that the millennial generation agreed that m-wallets were safe to use and Chawla and Joshi (2019) indicated that PS and trust have a positive relation among themselves. The PS construct is the main factor which determines the user’s intention because it is the only factor influenced by friends, peers and relatives (social influence). Whereas, Aydin and Burnaz (2016) said that subjective security was not an important factor while accepting M-wallets, as people were more concerned about the usefulness and ease of use of these M-wallets. Seetharaman et al. (2017) took security as a hygiene factor and resulted that security is the main key indicator which people check while adopting wallets, as they are worried about losing their personal details, whereas Husainah et al. (2022) concluded that security and trust have a positive relation, i.e. if these applications provide high security, people will have more trust among them, but on the other hand, security has no influence on intention and attitude. Karim et al. (2022) proved that security and PEOU had a positive relation. Kapoor et al. (2020) have reported that security was the most significant dimension in influencing M-wallet service quality. Chawla and Joshi (2020) also found that security was the main factor which influenced attitudes and BI towards the adoption of M-wallets. Security was more significant in males than in females, as males were prone to use these technologies for various transactions. Muhtasim et al. (2022) said that if services providers provide additional information about security measures, it will enhance the creditability of online payment systems.

1. Satisfaction

Kotler (1994), defined consumer satisfaction as “a person’s feeling of pleasure or disappointment resulting from comparing a product perceived performance (or outcome) in relation to his or her expectation”.

• Performance < Expectation: Dissatisfied

• Performance = Expectations: Satisfied

• Performance > Expectations: Delighted

Source(s): Authors’ collection

In other words, satisfaction is a feeling of pleasure resulting from comparing a product’s continued performance to initial expectations (Setiawan and Trianasar, 2023). In this study, we defined satisfaction as a mobile wallet user’s overall positive or negative feeling for the mobile service provided by the wallets. Satisfaction is how customer’s rates the brands or products based on all encounters and experiences. Previous studies have indicated that positive consumer satisfaction led to the formation of a positive attitude and repurchase intention (Amin et al., 2014; Purnama et al., 2021; Purnana et al., 2021). A study on the millennial generation of Jakarta found that if recovery actions were quick from the service providers, then it will lead to higher satisfaction, which will further lead to promotion of these services via positive word of mouth by a delighted or satisfied customer. The main key factor which is directly related to satisfaction was trust (Karim et al., 2022), and studies also proved that the higher the trust (Amin et al., 2014) and security (Karim et al., 2022), the higher will be satisfaction among consumers. The authors Muntasim et al. (2022) also found that some other factors which had a positive impact on satisfaction were higher transaction speed, higher authentication and a stronger encryption mechanism. Further, PU had no impact on satisfaction (Karim et al., 2022), but in the study of (Amin et al., 2014) PU indicated a positive relationship with satisfaction and PEOU also had a positive impact on satisfaction. Gupta et al. (2023) analysed the behaviour of tourists in Uttarakhand towards the usage of mobile wallets and found that they were satisfied with the services provided by these wallets.

1. Behavioural intention (BI)

Behavioural intention (BI) is defined as the willingness of an individual to use or continue to use technology. Alaeddin et al. (2018) said that perceived usefulness and security had a very strong influence, but trust and perceived ease of use had no influence on behavioural intention. Further, Hasan and Gupta (2020) did a study on tourist’s behavioural intention towards use of selected mobile wallets and found that if these wallets are user-friendly and provide some financial value or product-related value from their usage, then consumer will have positive behaviour towards these wallets and social influence is key factor which helps in creating positive behaviour; additionally, Tian et al. (2023), did study on Alipay wallet in Malaysia, which indicated that higher the behavioural intention, the more will be usage of Alipay among people. Pertriwi et al. (2020) indicated that BI had a positive impact on usage and PU has positive impact on BI, i.e. higher the PU higher will intention to use E-wallets in future. The other factors that influenced BI were perceived risk, PEOU, PU and enjoyment. But trust (Putra and Salim, 2023) and security (Chawla and Joshi, 2020) were found to be insignificant in these studies.

The objective of the present study was to find out the factors which impact consumer satisfaction of Generation Y using m-wallets. Firstly, the study examines the factors which impact customer satisfaction, further leading to behavioural intention. A descriptive research design was adopted to achieve the objectives of the study.

A survey was done among Millennials of Chandigarh from January 2024 to May 2024. Millennials (Generation Y) are people born approximately between 1981 and 1996. Malhotra (2010) recommended a sample of at least 200 for the SEM model having five or fewer constructs, with each construct having at least three measured variables. However, considering the probability of non-response rates and incompleteness, the sample size of 250 was finalized for the present study.

The survey was done using both online and offline surveys. The authors adopted non-probability sampling technique to collect the data. The questionnaires were sent in the form of Google Forms via email and WhatsApp. A total of 250 questionnaires were distributed, out of which 220 responses were administered for further results.

The present study employed covariance-based SEM to test the hypotheses of the study. Therefore, the analysis was performed using IBM AMOS version 23 in two stages, i.e. measurement model analysis and structural model analysis (Byrne, 2010). However, prior to this analysis, normalcy tests, construct reliability and common method bias were also tested.

The present study used standardized scales to measure the five constructs of the study, i.e. PU, PEOU, PS, satisfaction and BI (Table 3). On a five-point Likert scale, responses were obtained from the respondents (where 1 = strongly disagree and 5 = strongly agree).

There were 220 valid responses, of which 54.54% were female and 45.45% were male. Most of the respondents to the survey were between the ages of 20 and 30 years. About 50.09% of the study sample were employed, whereas 31.36% were students, 13.63% were self-employed and the remaining 4.09% were housewives (refer to Table 4).

To assess the construct reliability, the Cronbach’s alpha coefficient was computed. coefficient. According to Nunnally and Bernstein (1994) and Hair et al. (2010), a construct’s measurement is considered reliable if its Cronbach’s alpha value exceeds 0.70. The Cronbach’s alpha of the present study ranged from 0.854 to 0.899 (refer to Table 5), thereby confirming the construct reliability of the scale of the study. Additionally, prior to final data analysis, the test of normalcy was performed in order to check whether the fundamental premise of normality had been met. Skewness and kurtosis were taken into consideration for the same, which were less than 1, confirming the range prescribed by West et al. (1995).

Common method bias was assessed using Harman’s single factor test (Podsakoff and Organ, 2003). The test of the study confirmed no biasness in the study, as the total variance extracted by a single factor amounted to 39. 094%, which is within the accepted threshold of less than 50% (Podsakoff and Organ, 2003).

Measurement model analysis includes testing the overall model fit and construct validity (Hair et al., 2010). To assess the model fit, the values of Chi-Sq./df, comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA) and goodness-of-fit (GFI) were checked. The values of all the model fit indices were within the acceptable threshold values suggested by Malhotra (2010) (refer to Table 6). Therefore, model fit was achieved.

Further, construct validity was assessed by testing the construct for convergent and discriminant validity. For convergent validity, the values of composite reliability (CR) and average variance extracted (AVE) of each construct should be more than 0.70 and 0.50, respectively (Malhotra, 2010). In the current study, all constructs CR and AVE values exceed the threshold limit, exhibiting their convergent validity (refer to Table 7). Moreover, the discriminant validity was tested by applying the (1981) criterion. This criterion establishes discriminant validity if the square root of each constructs AVE exceeds its correlation coefficient with other constructs (Fornell and Lacker, 1981). The results of the present study indicate that the square root of each constructs AVE is higher than its correlation with other construct, demonstrating the discriminant validity between the constructs (refer Table 7).

The model fit indices the structural model was found within the threshold values (CMIN/DF = 1.812, CFI = 0.921, TIL = 0.913, RMSEA = 0.065 and GFI = 0.816), suggested by Hu and Bentler (1999). Therefore, the model is adequate for hypothesis testing (refer Table 8).

Hypothesis testing revealed that PU shows no significant influence on customer satisfaction (β = 0.083, p = 0.101). Hence, rejecting H1. In contrast, both PEOU and PS exert significant and positive effects on satisfaction (H2: β = 0.104, p < 0.05) (H3: β = 0.058, p < 0.05). Therefore, accepting H2 and H3. Lastly, H4 was also accepted, showing that customer satisfaction has the highest significant and positive impact on behavioural intention (H4: β = 0.085, p < 0.05).

The present study explored the factors which the behavioural intention of Generation Y while adopting mobile wallets (see Figure 1). The model was studied in two parts, i.e. firstly, the factors which impact consumer satisfaction and further leading to behavioural intention (refer to Figure 1). The study adopted the TAM model as the foundation base for the proposed model of the study (Davis, 1989). The authors have added satisfaction as a predictor of the intention in the current study.

About 88% of respondents used mobile wallets, while 12% did not prefer them. Google Pay 45 was the most popular mobile wallet, followed by PayTm and PhonePe. About 35% of users accessed their mobile wallets 2–6 times a week, and 32% used them more than ten times a week. Most respondents had been using mobile wallets for the past two years. The main reasons for their preference were convenience and time-saving features, with most people learning about mobile wallets through friends and social media.

The structural equation model results confirmed a positive and significant relationship between satisfaction and behavioural intention. It means that a higher level of satisfaction among consumers will lead to positive behavioural intention. These findings were consistent with the prior work done by Amin et al. (2014) and Purnama et al. (2021). It was found that PU had no impact on CS, leading to the rejection of H1. Our findings were also the same with the results of Chawla and Joshi (2020) and Husainah et al. (2022), but these findings were contradicted by the prior studies done by Yang et al. (2021), Kavitha and Kannan (2020), Shaw (2014), Seetharaman et al. (2017), Gawas (2022) and Husainah et al. (2022). Additionally, PEOU and PS both had a positive impact on CS (accepting H2 and H3). These results were in line with the study of Shin (2009), Yang et al. (2021), Chawla and Joshi (2020), Cambell and Singh (2017), Singh et al. (2019), Kavitha and Kannan (2020) and Tian et al. (2023). This indicates that people give more preference to ease of use and security of mobile wallets over usability. These findings emphasize that usability and security, rather than perceived usefulness alone, are the pivotal levers for fostering satisfied users who are inclined to stick with the service. Hence, the M-wallet provider should focus more on increasing customer satisfaction.

Based on the study’s findings, several key implications emerge for theory, practice and policy. Theoretically, this research extends the Technology Acceptance Model (TAM) by incorporating customer satisfaction as a mediating factor between user perceptions and behavioural intention. The non-significant impact of perceived usefulness (PU) on satisfaction challenges a core TAM assumption, suggesting that in the context of Generation Y mobile wallet users, factors like ease of use (PEOU) and security (PS) play a more vital role in shaping satisfaction. This shift calls for a re-examination of TAM in digitally fluent populations. Practically, mobile wallet service providers must focus on enhancing the user interface for simplicity and strengthening security measures to foster satisfaction, which in turn drives continued usage. Since customer satisfaction emerged as the most significant predictor of behavioural intention, improving user experience and ensuring trust should be central to service development. Additionally, marketers should leverage peer influence, as most users became aware of mobile wallets through social contacts and digital platforms, indicating that referral programmes and influencer marketing could effectively drive adoption. From a policy perspective, there is a need for frameworks that ensure standardized digital security protocols and promote ease of use, especially in financial transactions. Government and regulatory bodies should also focus on digital literacy campaigns that highlight the safety and benefits of using mobile wallets.

In conclusion, this study provides valuable insights into the factors influencing mobile wallet adoption among Generation Y consumers, emphasizing the critical role of customer satisfaction in driving behavioural intention. While traditional TAM constructs like perceived usefulness did not significantly impact satisfaction, ease of use and security emerged as key contributors. These findings suggest that in an increasingly digital and experience-driven economy, trust and usability are more influential than functionality alone.

Despite the study’s contribution to the existing literature on satisfaction, the study also has some limitations, providing opportunities for future research. First, the ]study has made an attempt to study the factors which lead to customer satisfaction. However, it is pertinent to note that the sample used in the study was based on non-probability sampling from one geographical location and one generation (Millennials) only. Therefore, the findings reported in the present study must be validated with other samples. Second, the study primarily drew a relationship between PU, PEOU, PS and CS, further leading to BI, which has been adapted from the TAM model. However, the literature also indicates other theories also, such as the UTAUT model and the theory of planned behaviour, which can be integrated to study the relationship between technology usage and the behaviour of customer towards technology. Third, the variable satisfaction is taken as an antecedent of behavioural intention in the study. However, the future researchers are encouraged to take satisfaction as a mediating variable to explore the impact of it on other constructs.