Editorial Board

A Refereed Monthly International Journal of Management

Navigating the Digital Wallet Landscape:

A Study of Youth E-wallet Migration Patterns

 

 

1. Kajol

Research Scholar

Department of Management Studies

Indian Institute of Information Technology Allahabad

Email:rsm2018003@iiita.ac.in

ORCID: https://orcid.org/0000-0003-3000-8110

Jayshree Bhattacharjee

Assistant Professor

Symbiosis School of Banking and Finance

Symbiosis International Deemed University

Lavale, Pune, India.

Email: jayashree.bhattacharjee@ssbf.edu.in

ORCID:https://orcid.org/0000-0001-9942-1996

 

Lata Kumari Pandey

Research Scholar

Department of Management Studies

Indian Institute of Information Technology Allahabad

Email: rsm2022503@iiita.ac.in

ORCID:https://orcid.org/0000-0003-0771-0649

Corresponding author

 

Ranjit Singh

Professor

Department of Management Studies

Indian Institute of Information Technology Allahabad

Email: ranjitsingh@iiita.ac.in

ORCID: https://orcid.org/0000-0001-9408-9525

 

Abstract

 

This study utilizes primary datagathered via a structured questionnaire from 324 e-wallet users in Uttar Pradesh, India. Using multiple regression analysis, the study identifies factors influencing youths' switching intentions among e-wallets through the pull, push, and mooring (PPM) approach. The findings highlight that the attractiveness of alternative e-wallets is the most significant factor prompting users to switch. The study's limitations include its focus on India, necessitating further research for broader generalization, and the exclusion of other potential antecedents and mediation/moderation effects. Practical implications suggest that e-wallet providers should use social media to highlight appealing features and design visually attractive, customer-centric e-wallets. This pioneering research addresses the under-explored area of e-wallet switching behavior, offering original insights.

 

Keywords: Switching Intention;Youth;E-Wallet; Push Pull Mooring Model; PPM.

 

 

Introduction

Digital payments, a cornerstone of modern financial transactions, have evolved significantly over time. Originating from electronic funds transfer systems in the mid-20th century (Barnes, 2018), their widespread adoption surged with the internet's rise and technological advancements in the late 20th and early 21st centuries.E-wallets, an essential component of digital payments, allow users to store, manage, and conduct financial transactions electronically via mobile apps or online platforms (PayPal, n.d.). They store payment details securely, including credit cards, bank accounts, and digital currencies, providing a convenient and secure payment method (PayPal, n.d.).

The concept of e-wallets gained prominence with e-commerce and online banking’s emergence in the late 1990s and early 2000s (PayPal, n.d.). PayPal, founded in 1998, was pivotal, in revolutionizing online payments globally (PayPal, n.d.). Its secure money transfer capabilities paved the way for widespread digital wallet adoption.Today’s e-wallets have evolved with advanced features like loyalty programs, rewards, mobile payments, and diverse payment method integration (Dahlberg et al., 2008). They facilitate transactions across consumer-to-consumer, consumer-to-business, and online channels (Dahlberg et al., 2008; Lee, 2019; Shin, 2009).

In India, digital payments have seen remarkable growth due to factors like high internet connectivity, mobile data accessibility, robust wireless networks, legislative support, and financial inclusion initiatives (Patil et al., 2020; Pal et al., 2020; Singh & Sinha, 2020). India’s Digital Payment Index (DPI) in 2023 stood at 418.77, with a robust annual growth of 10.94% (MCIR, 2024). This growth is significant, considering India accounts for 46% of global digital transactions (RBI, 2024).

Younger consumers, driving this digital payment surge, favor e-wallets and internet banking (Katz & Aspden, 1997). Their inclination toward internet banking is expected to rise, emphasizing the need for retention strategies due to their low brand loyalty (Akturan & Tezcan, 2012; RBI, 2020). Despite lower disposable incomes, their substantial discretionary income fuels frequent shifts between digital payment technologies (Sum Chau & Ngai, 2010; Pandey & Singh, 2024). In 2021, 65% of young millennials and 57% of Gen Z were using e-wallets (GlobalPayments, 2022). This popularity intensifies competition among providers, necessitating a deep understanding of customer switching behavior (Sinha &Shanakar, 2017). Retaining customers and grasping their decision-making dynamics is crucial amid intense market dynamics (Liu et al., 2021; Salo & Makkonen, 2018; Wang et al., 2019; Pandey et al., 2025a).

India’s dynamic digital payment landscape, shaped by technology, regulations, and consumer preferences, highlights the strategic importance of user retention and understanding switching dynamics. This study delves into young e-wallet users’ switching intentions and identifies key influencing factors, employing the push-pull paradigm to explore user behavior within the digital payments realm.The present study aims to achieve two primary objectives: first, to assess the switching intentions of young customers towards e-wallets; and second, to identify the key variables that act as barriers to their switching behaviour.This study addresses three research questions:

• What variables influence their decision to move from one e-wallet to another?
• Do these Variables have equal weight?
• What is the overall effect of these variables on the intention to switch?

This research is the first in India to explore e-wallet switching behaviuor among youth. In addition, this is the first study that applies the pull, push, and mooring (PPM) approach in case of India. The PPM model has been regarded as one of the most extensive approaches to accessing switching behavior(Bhattacherjee & Park, 2014; Wu et al., 2017; Lin et al., 2021; Bhattarcaharjee et al., 2024), but it has not been used in the context of e-wallets. Thus, by applying the PPM model, the study aims to identify and assess the factors that influence youth switching behaviour in India.

Literature Review

Numerous studies have been conducted into e-wallets since their inception in 1997, acknowledging them as a flagship product of FinTech (Yolanda &Koesrindartoto, 2019). E-wallets encompass a broad concept, serving as a digital means to store money and conduct transactions across devices like computers, smartphones, and tablets (Chawla & Joshi, 2019). They also allow users to manage personal information and various payment methods, akin to an extension of online banking. While previous literature primarily focused on e-wallet adoption and the challenges therein (Di Pietro et al., 2015; Kapoor et al., 2013; Liébana-Cabanillas et al., 2015; Mun et al., 2017; Ozturk et al., 2017; Kajol et al., 2022), recent studies have shifted attention towards understanding the barriers and facilitators of digital payments, including e-wallet usage (Alalwan et al., 2016; Chawla & Joshi, 2019; Foroughi et al., 2019; Liébana-Cabanillas et al., 2020; Makanyeza, 2017; Singh et al., 2020; Sinha et al., 2019; Wang & Lai, 2020).

However, the phase of switching between e-wallets has been relatively overlooked in research (Zhou, 2013; Lu et al., 2017). Limited models or theories exist to measure this behavior among e-wallet users. One such model gaining traction is the PPM (Push-Pull-Mooring) model, originally derived from Heberle’s pull-push paradigm and Ravenstein’s Laws of Migration (Heberle, 1938; Ravenstein, 1885). While the PPM framework has been successfully applied in various online service contexts such as instant messaging, social networks, cloud services, and online gaming, its application to e-payment domains and specifically to e-wallet switching behavior remains scarce (Fang & Tang, 2017; Sun et al., 2017; Li & Ku, 2018; Xu et al., 2014; Bhattacherjee& Park, 2014; Wu et al., 2017; Hou et al., 2011; Lai et al., 2012; Chen & Keng, 2019; Lin et al., 2021; Hsieh et al., 2012; Chen & Chen, 2010; Ilgen et al., 2011).

This study uses the PPM model to examine factors influencing users' intentions of switching e-wallets. While the PPM framework has been previously used in mobile payment research, its application to e-wallet switching behaviour in the Indian context remains unexplored, making this study a novel contribution (Sun et al., 2017; Chen &Keng, 2019; Lin et al., 2021). By identifying the key drivers behind consumers’ switching intentions, this research offers deeper insights into user behaviour within the evolving digital payment landscape.

 

Push Factors

A push factor is the “factor that motivates people to leave the origin” (Stimson & Minnery, 1998). In service research, the confrontational correlation between satisfaction with the service provider and switching intention is well documented (Bansal & Taylor, 1999; Cronin et al., 2000). Based on the above understanding, the authors have considered subscriber satisfaction as a push factor.

• Subscribers’ satisfaction

Subscribers’ satisfaction has been regarded as a push factor in numerous previous studies (Bansal et al., 2005; Zhang et al., 2008; Chuang, 2011; Stimson & McCrea, 2004). It is the discrepancy between what a consumer anticipates from a product (i.e. before consumption) and the product's absolute efficacy (i.e. after consumption) (Bhattacherjee, 2001; Kim et al., 2007)likewise, customers are pleased when they acknowledge that a product or service matches or outweighs their expectations in terms of reliability or potency.The above literature helps to frame our first hypothesis:

• H₀₁: There is no significant association between ‘Subscriber’s Satisfaction’ and their behavioral intention to switch their e-wallet.

Pull Factors

Pull factors are “positive factors drawing prospective migrants to the destination”(Moon, 1995, pp. 507). As it is a formative rather than a reflecting entity, the pull effect is conceptualized theoretically and empirically as a cluster of indications or situations that prompts or nudges customers to switch to a different service provider(Nimako & Ntim, 2013).

• Alternative Attractiveness (AA)

From the standpoint of PPM, the availability of alternative destinations is a pull factor because it has a beneficial influence on enticing migrants from distant locations(Moon, 1995). Alternative attractiveness arises from the comparison when potential customers perceive the pivotal traits of an alternative service to be preferable; they are more inclined to switch to a certain choice(Jones et al., 2000; Zengyan et al., 2009). This literature helps to frame our second hypothesis. 

• H₀₂: There is no significant association between ‘Alternative Attractiveness’ and their behavioral intention to switch their e-wallet.

Mooring Factors

Mooring factors refer to individual-specific psychological elements that either hinder orfacilitate the intention to switch. These factors play a crucial role in shaping switching decisions, consciously or unconsciously, and can exert both positive and negative influences on user behaviour.

• Switching Cost and Habit strength

Switching costs are the trade-offs or obligations that prevent clients from switching offerings(Jones et al., 2007), and encompass not only financial consequences but also due process and interpersonal costs.(Burnham et al., 2003). The study included proposes switching costs as a mooring factor based on the study conducted by Sun et al., (2017). According to past researchers, the habit has a substantial impact on switching intentions, which is a prelude to behavioural intentions(Jolley et al., 2006; Liao et al., 2006; Yanamandram & White, 2006; Pandey et al., 2025b). Habit is repeated behaviour, an expression of instinctive and spontaneous reflexive behaviour (Pandey et al., 2024; Pandey et al., 2025c). When users become accustomed to something, they are less motivated to seek alternatives.These studies provide the background for our third and fourth hypothesis.

• H₀₃: There is no significant association between ‘Switching Cost’ and their behavioral intention to switch their e-wallet;
• H₀₄: There is no significant association between ‘Habit Strength’ and their behavioral intention to switch their e-wallet.

Switching from one incumbent e-wallet to an alternative one is an analogous process, even when it does not involve any physical movement (Lo &Santoso, 2020). The PPM model is most relevant for explaining e-wallet switching. Figure 1 describes the proposed study paradigm.

Figure 1: Proposed Framework

 

Data and Methodology

 

Primary data has been collected from the Indian state of Uttar Pradesh, India. It has a total area of 243286 km² and is divided into 18 divisions and 75 districts (Kumar et al., 2019). Uttar Pradesh, in particular, was chosen as a case study for four reasons: i) It is the most populous state in India; ii) It is also an IT-HUB of North India and the second-largest economy in India (Bala & Singhal, 2018); iii) It has the highest proportion of young people, and iv) As per the census 2011, 77.73 percent of the state population lives in rural areas. The study has been conducted on the youth of Uttar Pradesh. ‘Youth’ is often defined as the age whenan individual leaves compulsory education, and finds his or her first job (National Youth Policy 2014, India). Different countries define the youth age group differently. Some proportion of Generation Y and Generation Z can also be represented in the given age group. As a result, a study on youth could help researchers delve deeper into the subject.

The online collection of data was done using a structured and close-ended questionnaire which is shared through online mode with around 2000 people. A sample of 360 respondents was surveyed, yielding 324 valid replies for this study. The profile of the respondent is given in Table 1. 

Table 1: Profile of the respondents

Variables	Frequency	Percentage (%)
Gender Male Female	186 138	57.41 42.59
Age 15-29 years	324	100
Usage Frequency Once a week More than once a week Once a month	94 147 83	29.02 45.27 25.62
Usage Experience < 6 mnts 6 mnts - 1 yr 1 yr - 2 yrs 2 yrs<	80 57 62 125	24.69 17.59 19.14 38.58

Source: Compiled by authors

Individuals using e-wallets were the sampling unit of the study. There were two partsto the questionnaire. In part one of the questionnaire,some demographic and basic information related to e-wallet usage are collected. The second part of the questionnaire was a Likert-type scale having 36 statements with 5 scale points for each of the statements. The statements used in the scale are all positive and hence the options are numbered from 1 to 5 with the increasing level of their experience.The questionnaire is given in appendix 1. Since the researcher is interested in knowing “the intention to switch” of e-wallet users so that is the response variable (dependent variable) of this study.

Results

Table 2 shows the scale's reliability, assessed by Cronbach’s Alpha. Cronbach’s Alpha coefficients above 0.70 confirm the scale's strong internal consistency.

 

 

Table 2: Reliability Statistics

Constructs	Cronbach's Alpha	No. of Items
Switching Cost	0.802	11
Users Satisfaction	0.863	7
Alternative Attractiveness	0.837	5
Habit Strength	0.918	8

Source: Compiled by authors

Multiple regression is used to find e-wallet switching factors. For a given respondent, the sum of scores across the first five statements provides a quantification of his/her intention to switch. Similarly, the sum across the next 11, 7, 5, and 8 statements provide the scores related to the factors. The scores can be attained, given the fact that Likert Scales are summative. So, the score for Switching Intention i.e., the sum of scores from the responses from 1 to 5 is taken as the dependent variable. The independent variables comprise Switching Cost, User Satisfaction, Alternative attractiveness, and Habit Strength which are also obtained by summing up the responses of an individual corresponding to the statements mentioned above. Eventually, a multiple regression equation is fitted using the dependent and independent variables mentioned. The general form of the regression equation used is as follows:

…(1)

The outcome of the multiple regression analysis with “intension of switching (switch_int)” as the dependent variable and factors like Switching Cost (switch_cost), Subscriber satisfaction (user_sat), Alternative attractiveness (alt_attr), and Habit Strength (habit_str) as the independent variables.The model also includes a constant term and an error term. The results of the multiple regressions are summarized in Table 3.1-3.3.

Table 3.1:  Regression analysis

Regression Statistics
Multiple R	0.533772753
R Square	0.284913351
Adjusted R Square	0.275946748
Standard Error	3.358951375
Observations	324

Source: Compiled by authors

Table 3.2: The regression ANOVA

ANOVA
	Df	SS	MS	F
Regression	4	1434.01	358.5026	31.77495
Residual	319	3599.135	11.28255
Total	323	5033.145

Source: Compiled by authors

Table 3.3: The coefficients of the regression equation

	Coefficients	Standard Error	t Stat	P-value
Intercept	5.05780678	1.551189	3.260601	0.001232
Switch_Cost	0.016374025	0.035579	0.460219	0.645672
Subs_Sat	-0.06625793	0.060225	-1.10018	0.272084
Alt_attr	0.67721803	0.063476	10.66886	6.29E-23
Habit_Str	-0.02260381	0.048069	-0.47024	0.638507

Source: Compiled by authors

In the Table 3.1 regression analysis showsthe value of R²as 0.2849 which indicates that the independent variables can explain only 28.49 % of the variation in the dependent variable i.e. intention to switch. Table 3.2 displays the ANOVA concerning regression and based on F-value the regression coefficient is significant.Table3.3provides us with the estimated regression equation which is given as follows:

…(2)

 

 

 

Note: *** significant

Figure 2: Results of Analysis

 

The results of hypothesis testing are given in Table 3.3. Out of the independent variables only “Alternative attractiveness” has a p-value of less than 0.05 i.e. (p-value 6.29x10^-23) indicating that this is the only significant variable among the independent variables. Other independent variables like the “Switching Cost”, “Subscriber Satisfaction” and “Habit Strength” do not influence “Intension of Switching”.  Also, it might be noted that the coefficient corresponding to “Alternative attractiveness” is positive (as well as significant) this indicates that with other e-wallets becoming more appealing visually and offering enhanced features the chance of switching of e-wallet of the respondent increases.

Discussion

 

The research findings highlight the significant impact of alternative attractiveness on users’ behavioral intention to switch to an e-wallet in India. However, the study also reveals that switching cost, user satisfaction, and habit strength do not significantly influence users’ intention to switch. This contradictory finding has implications for understanding consumer behavior in the e-wallet market.

The non-significant effect of users’ satisfaction with their current e-wallet on switching intention contradicts some existing studies (Bansal et al., 2005; Zengyan et al., 2009; Zhou, 2015; Fu, 2011) but aligns with findings by Sun et al. (2017) and Fan et al. (2021). This inconsistency may stem from the fact that user satisfaction is subjective and based on individual preferences and experiences. Some users may prioritize specific features or benefits offered by alternative e-wallets, leading them to switch despite being generally satisfied with their current provider (Chawla & Joshi, 2019).

Similarly, the study’s finding regarding the non-significant impact of switching costs on switching intention contradicts prior research (Bansal et al., 2005; Fan et al., 2021; Hsieh et al., 2012; Kuo, 2020; Lai et al., 2012; Sun et al., 2017). In India’s evolving e-wallet market, where new providers and features frequently emerge, switching costs might be mitigated by offers like incentives or promotions, reducing perceived barriers to switching (Sinha &Shanakar, 2017).

The lack of significant influence of habit on switching intention is consistent with some studies (Sun et al., 2017; Hsieh et al., 2012; Lai et al., 2012), but contrasts with findings by Loh et al. (2020) and Marseto et al. (2019). India’s digitally savvy population, particularly among the younger demographic, may be more open to exploring new technologies and platforms, reducing the impact of habit strength on switching behavior (Katz & Aspden, 1997).

In contrast, alternative attractiveness significantly influences users' intention to switch, aligning with previous studies (Keaveney, 1995; Wieringa & Verhoef, 2007; Lai et al., 2012; Wang et al., 2019; Bansal et al., 2005; Fan et al., 2021; Kuo, 2020; Loh et al., 2020; Fu, 2011; Chang et al., 2017). Intense competition among e-wallet providers in India drives continuous innovation and improvements, enticing users towards alternative options. Users’ perceptions of the value offered by alternative e-wallets, such as better security and rewards, can also outweigh considerations like switching costs or habit strength, prompting them to switch if they perceive significant value.The overview findings are presented in Figure 3.

 

Figure 3: Summary of overall findings in Model

 

Theoretical Implications

Existing literature extensively covers digital payment adoption and usage in India, overlooking e-wallet sustainability. Studies have not explored switching intentions among e-wallet users in India, specifically in the state of Uttar Pradesh. This research fills this gap by identifying barriers to e-wallet switching and highlights that unique features can deter switching, emphasizing continuous innovation's role in retaining users. The PPM model effectively measures switching intentions and can extend to analyze transitions to new payment modes like biometrics and face recognition systems. Given the rapid evolution of digital payments, future research should use this model to study user transitions from e-wallets to emerging payment methods, promoting a comprehensive understanding of user behavior in this dynamic sector.

Practical Implication

The study emphasizes that alternative attractiveness plays a vital role in enhancing customer retention, suggesting that service providers should prioritize this aspect over customer habits and switching costs, which were found to be insignificant in influencing switching intentions. Despite younger customers having lower disposable incomes, they possess substantial discretionary income and purchasing power, making them a lucrative target market (Sum Chau & Ngai, 2010). Leveraging the internet and social media, which are widely accessed by the Indian population, especially the youth, can be instrumental in attracting customers. Communicating appealing features tailored to customer needs and preferences is crucial. Regular updates and additions to e-wallet features should align with user expectations (Singh & Choudhury, 2016).

Expanding e-wallet services to rural areas, incorporating easy payment features like face recognition and biometric payments, and encouraging merchants to accept multiple e-wallets can enhance accessibility and usability. Leveraging positive word of mouth from merchants for promotion can further boost e-wallet adoption (Kajol & Singh, 2022). Collaborative efforts between service providers, policymakers, and merchants are essential to make e-wallets more appealing, user-friendly, and widely accepted across diverse demographics and geographic locations.

Limitations and scope of future research

The study examined the issue from the perspective of Indian users, with data collected specifically from young respondents residing in North India. As a result, the findings may not fully capture variations in perceptions regarding the tendency to switch to e-wallets across different countries or among other age groups.Second, the model of PPM does not stipulatethe use of fixed factors: push, pull, and mooring.The study has been conducted by considering only four factors. There are other antecedents that are not referenced in the present study that could have profoundly impededthe intention to switch e-wallets. Third, to mitigate the inadequacyof past studies that researched electronic payment systems from a comprehensive viewpoint, the present study centered on e-wallets. However, another mode of electronic payments has been neglected because of such an approach. This study does not incorporate the mediation and moderation effects of influencing factors. Future researchers are encouraged to explore these effects to gain deeper insights and enrich the understanding of the underlying relationships.

 

Conclusion

The growing use of smartphones and enhanced internet accessibility has led to a significant rise in the popularity of digital payment options, like e-wallets.E-wallets offer direct access via mobile and PC, facilitate communication with banks and retailers, and manage data sharing among users, issuers, and service providers. However, understanding why users switch to e-wallets is crucial for service providers.

This study employs the Pull, Push, and Mooring approach (PPM) to delve into switching behavior among Indian youth, focusing on factors influencing this behavior. Through primary data from 324 e-wallet users in Uttar Pradesh, India, the study evaluates the model’s empirical strength. It reveals that alternative e-wallet attractiveness is paramount in driving switches, with the pull factor of alternative attractiveness reducing switching intentions, consistent with prior research (Keaveney, 1995; Wieringa & Verhoef, 2007; Lai et al., 2012; Wang et al., 2019; Bansal et al., 2005; Fan et al., 2021; Kuo, 2020; Loh et al., 2020; Fu, 2011; Chang et al., 2017).

Contrary to past studies, this research finds that switching costs do not significantly impact switching intentions in India. Users tend to adopt e-wallets without switching costs, and since many e-wallets are free of monetary charges today, the influence of switching costs is negligible. Additionally, the study does not observe a link between habit and switching intentions in India, deviating from previous findings.

The study’s implications offer valuable insights for digital payment providers and governments, emphasizing the importance of understanding and leveraging e-wallet attractiveness to retain users and enhance services. However, the study acknowledges limitations, indicating that findings may vary with geographic location or methodological variations. Future research could build upon these insights by addressing these limitations for a more comprehensive understanding of e-wallet switching behavior.

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Appendix 1: Statements used in the Questionnaire

S. No.	Items
1.	Probability to switch to another e-wallet from the current e-wallet.
2.	Determined to switch to another e-wallet from the current/existing e-wallet.
3.	Switching from one e-wallet to another e-wallet is a wise idea.
4.	Planning to switch to a new e-wallet after the money kept in the current/existing e-wallet will be used.
5.	Planning to switch to a new e-wallet when the subscription in the current/existing e-wallet will be expired.
6.	Preparing documents while switching to using another e-wallet is troublesome.
7.	Trouble in developing relationships again with a new e-wallet.
8.	Time-taking switching procedures.
9.	The trouble with learning the functions/operations/services of the new e-wallet.
10.	A new e-wallet is comparatively more difficult.
11.	The collection of information for a new e-wallet takes time and energy.
12.	Discounts or contract continuation benefits provided by the existing e-wallet are poor.
13.	Lack of service from the personnel of the existing e-wallet.
14.	Fear of losing the relationship with the current e-wallet service provider.
15.	Support the current/existing e-wallet service provider.
16.	Current/existing e-wallet service providers have a good corporate image.
17.	Satisfaction in keeping many e-wallet /payment channels.
18.	Bill accuracy of current/ exiting e-wallet is satisfactory.
19.	The time taken by the current/existing e-wallet to deal with complaints is satisfactory.
20.	Satisfactory diverse subscription plans of current/ existing e-wallet.
21.	The service quality of the current/existing e-wallet is satisfactory.
22.	The connection frequency of the current/existing e-wallet is satisfactory.
23.	Attitudes of the customer service personnel toward current/ existing e-walletsare satisfactory.
24.	The attractiveness of promotion of other e-wallets.
25.	Other e-wallets’ promotion meet needs better.
26.	The attractiveness of services provided by other e-wallets.
27.	The features and services of other e-wallets are delightful.
28.	The visual attractiveness of other e-wallets.
29.	The current/existing e-wallet comes to mind while making payments for services.
30.	The current/existing e-wallet comes to mind while making any banking transaction.
31.	The current/existing e-wallet comes to mind while making payments for products or services ordered online.
32.	The first service provider comes to mind whenever friends ask for a recommendation.
33.	The current/existing e-wallet comes to mind whenever someone (other than family and friends) asks for a recommendation.
34.	Current/existing e-wallet comes to mind while making payments at physical stores.
35.	Whenever a new service is introduced, the first provider comes to mind.
36.	The first provider comes to mind while setting up a new account.