Introduction

Mobile commerce, enabled by wireless networks and mobile devices, has overcome the constraints of transaction time and location (Holmes et al. 2013), providing users with great flexibility and convenience in consumption (Chi, 2018). With the vigorous development of mobile commerce, the number of mobile smart phone users is increasing day by day, and the new transaction payment method, mobile payment, has attracted attention (Wang and Dai, 2020). Mobile payment can be widely interpreted as a service that consumers use mobile phones, iPads and other mobile devices to conduct transactions and pay for consumption (Dahlberg et al. 2008). It can be implemented through near-field communication payments and fast response payments. Near Field Communication payments are made contactless via a Near Field Communication chip installed in a smartphone to enable transactions in fields such as physical store shopping or transportation services (Singh and Sinha, 2020). In the case of quick response payment, the user completes the transaction by scanning the quick response payment code using a smart device and manually entering the protection password or authentication (Liu et al., 2021). In other words, consumers utilize wireless and other communication technologies to complete transactional payments by scanning and identifying the payment barcodes provided by merchants through mobile applications on their mobile devices (Humbani and Wiese, 2019). Traditional shopping payments are typically made using cash, debit cards, or credit cards. Compared with traditional consumer payment methods, mobile payment has many advantages. For instance, users can complete payments anytime and anywhere (Mu and Lee, 2021), and the convenience and speed of payments greatly save time for both consumers and merchants (Oliveira et al., 2016). Innovative social enterprises have also launched payment service systems, such as Apple Pay and Google Pay in the United States and Alipay and WeChat Pay in China. In China, mobile payments have become increasingly popular. Mobile payment is increasingly being paid attention by merchants (Khalilzadeh et al., 2017), and some merchants even refuse customers to provide cash for transactions (Wildau and Jia, 2019). Alipay and WeChat Pay, owned by the two major companies Alibaba and Tencent, are widely used mobile payment platforms in the Chinese market. The COVID-19 pandemic has accelerated the adoption of mobile payments (Rafdinal and Senalasari, 2021; Upadhyay et al., 2022), payment barcodes can be seen everywhere in various types of stores or street vendors, providing convenience for consumer transactions. Despite its many conveniences, not all populations have embraced it (de Luna et al., 2019), and integrating advanced technology into the lives of older adults remains a challenge (Kim et al. 2016).

The number of elderly people in China has shown a significant increase in recent years. China Internet Network Information Center (2023a) reported that, as of December 2022, the usage rate of online payments among the senior aged 60 and above in China, with a population of 280.04 million and make up 19.8% of the population. As of June 2023, the number of non-Internet users in China is 333 million, and the elderly aged 60 and above are the main group of non-Internet users. The proportion of non-netizens aged 60 and above in the total number of non-netizens is 41.9% (China Internet Network Information Center, 2023b). Meanwhile, the total population in China adopting online payments reached 911 million. Moreover, In the first three quarters of 2022, the mobile payment business in China recorded 116.769 billion transactions with a total amount of 378.25 trillion CNY, year-on-year growth was 7.4% and 1.1%, respectively (The people’s bank of China payment and settlement department, 2023). It can be said that with the increasing development of mobile commerce, mobile payments will become more important in facilitating transactions between consumers and merchants (Afandi et al. 2021). However, the physical, psychological and social losses of older people may lead to different attitudes towards the future from those of younger people (Bergfrid et al. 2024), including towards future technology use. Although the number of elderly people using mobile phones has increased, it does not mean that they are willing to use mobile payment (Yang et al. 2023), after all, most elderly consumers prefer to use cash transactions (Cham et al. 2022). As the study of Li et al. (2020) points out, the use of mobile payment by the elderly over 65 is only one eleventh of that of the young under 25. The elderly are one of the important consumer groups in the market, but in the field of financial consumer technology, the elderly have not been fully paid attention and tapped (Chaouali and Souiden, 2019). Facing a large elderly population, to achieve sustainable development of mobile payment businesses, payment providers must find strategies to retain users (Yuan et al. 2020). Hence, the research topic of user mobile payment behavior is particularly important. Therefore, the first goal of this study is to focus on the elderly users in the context of mobile payment.

It is particularly important for service providers to identify the factors that drive users to use mobile payments (Malarvizhi et al., 2022). Currently, there is significant attention on the research of user behavior in mobile payments. First, at the regional level, research (Boden et al., 2020) shows that the adoption of mobile payments varies among countries around the world. Mobile payments are growing rapidly in countries such as China and Japan in Asia (Shin and Lee, 2021), and these Asian countries have the highest adoption rate, while North American countries have moderate adoption rate and European countries have low adoption rate (Shin and Lee, 2021). Then, in terms of specific content research, for example, Hameed et al. (2024) discussed the adoption of mobile payment system by tourists. Wang and Dai (2020) studied young Chinese consumers’ intention to use mobile payments in physical stores. The results show that perceived ease of use, perceived usefulness, attitude and personal innovation are the factors that affect user behavior. The study by Kim et al. (2010) explored the impact of demographic characteristics of Chinese users on their mobile payment adoption. In addition, Pousttchi and Wiedemann (2007) took German consumers as research objects, and the results showed that perceived usefulness and perceived ease of use had significant effects on users’ willingness to use mobile payment. Mobile payment adoption among the elderly remains low relative to the rest of the population (Wong et al., 2022). Mobile payment is not easily accepted by elderly users (Benson et al., 2019). In general, most existing studies focus on non-elderly people, and few studies focus on elderly people’s willingness to use mobile payment (Choudrie et al., 2018; Hanif and Lallie, 2021). Therefore, the second goal of this study is to focus on the elderly and explore the factors that drive the elderly to use mobile payment.

It seems logical to apply an already proven TAM to understand and predict consumer behavior toward mobile payment systems (Chen, 2008). The Technology Acceptance Model (TAM) (Davis et al., 1989), is an excellent model for explaining users’ technical behavior (Hsu and Lu, 2004). The TAM is considered to be the most robust and concise user technology acceptance model (Davis et al., 1989; Pavlou, 2003). However, TAM is oversimplified and additional variables need to be included based on the research context (Venkatesh and Davis, 2000). TAM has been extended to provide deeper insights into predicting user acceptance of relevant technologies (Martínez-Torres et al., 2015). Elliot and Loebbecke (2000) pointed out that the lack of consideration for social factors is one limitation of the TAM. At the same time, the TAM does not incorporate the perceived risk of negative beliefs into technology adoption. For example, Abikari et al. (2023) studied the impact of negative constructs on users’ adoption of e-banking. In the context of this study, mobile payment, as a new financial transaction service technology, and the uncertainty and adverse consequences of the new technology perceived by the elderly, make the elderly more obviously show an increase in risk perception (Kim and Yang, 2016). We believe that it is necessary to include perceived risk into TAM to explore whether the elderly have a negative impact on mobile payment intentions. Additionally, the Information Systems Success Model (ISSM) (DeLone and McLean, 1992; 2003), is an excellent theoretical framework widely applied in studies on users’ adoption of information technologies (Gao and Waechter, 2017). However, its application in the emerging technology of mobile payments is limited (Gao and Waechter, 2017), with only a few studies focusing on mobile payments, such as Gao and Waechter (2017) and Yuan et al. (2020). As a system, the information quality, system quality and service quality of mobile payment are crucial, which is precisely the important content of the successful model of information system. Therefore, it is necessary to apply the ISSM to the research of mobile payment. To sum up, the third goal of this study is to further expand TAM and ISSM on the theoretical basis and establish a mobile payment acceptance model for the elderly to predict and explain the mobile payment behaviors of elderly users.

As discussed above, the wide application and popularity of communication technology and smart phones provide a mobile payment platform for users’ offline consumption (Hussain et al., 2019). As mobile payments have gained significant popularity as a novel payment method, research on user behavior in mobile payments has received considerable attention. However, in the context of an aging population, mobile payment is not easily accepted by elderly users (Benson et al., 2019), and most of the existing studies focus on non-elderly people, and few studies focus on elderly people’s willingness to use mobile payment (Cham et al., 2022; Hanif and Lallie, 2021). At the same time, the TAM and the ISSM need to be further expanded and enriched. To this end, in the context of an aging population, the aims of our research is to try to reveal the factors that affect the senior’s mobile payment intention. Based on the integration of ISSM and TAM, this study adds additional variables such as perceived trust (PT), perceived risk (PR) and social influence (SI), to establish a mobile payment acceptance model for the elderly. The structural equation model was used to verify the mobile payment acceptance model for the elderly. The research further expands the application of TAM and ISSM in technical fields and user groups. The mobile payment acceptance model of the elderly constructed in this research can be used to explain and predict the mobile payment behavior of the elderly, and provide a theoretical basis for the research on technology acceptance of users. The research results can provide references for mobile payment technology system developers, designers, marketers and relevant policy formulation and management departments, and help the aging design of mobile payment technology system, bridge the digital divide of the elderly, and promote the healthy development of mobile payment.

The follow-up contents of this study are as follows: The second section introduces the theoretical basis and research hypothesis. Section three explains the research methodology, including respondents, research tools, and statistical analysis methods. The fourth section presents the results, including the measurement model and the structural model analysis results. The fifth section discusses the research results. The sixth section summarizes the conclusions, contributions, shortcomings and future research directions.

Theory and hypothesis development

Seniors and their mobile payment adoption

Population aging is one of the most significant social changes facing countries around the world today and in the future. The World Health Organization estimates that the number of people over the age of 60 worldwide will increase from 900 million in 2015 to 2 billion by 2050, 22% of the global population (World Health Organization, 2017). According to China’s seventh population census, there are 264.02 million elderly people aged 60 and above in China, accounting for 18.7% of the total population. It is obvious that China is facing the severe challenge of population aging (Tong, 2021). With the aging problem becoming more and more prominent, the aging of population has been paid more and more attention. In previous studies on the elderly, the age definition of the elderly is not uniform. For example, the elderly in Selwyn’s (2004) study are over 60 years old, and the elderly in Shoemaker’s (2003) study are over 55 years old. In psychology and human-computer interaction studies, older people are generally defined as those aged 60 and older (Righi et al., 2017). Therefore, based on the actual situation in China, the elderly in this study are defined as 60 years old and above.

The rapid development of smart phones and communication technology has promoted the development of mobile payment technology. These new technologies will transform gerontology, avoid increasing inequality and isolation (Fanning et al., 2024), and realize digital dividends and active aging for the elderly. Mobile payment is an innovative technology that has been widely recognized and accepted worldwide (Humbani and Wiese, 2019). Mobile payment allows users to carry out transactions free from time and place restrictions (Alalwan et al., 2017). It is a financial transaction in which the user initiates, authorizes and completes financial settlements through the use of electronic mobile devices (de Luna et al., 2019). However, digital literacy is lower among older adults (Haynes et al., 2021). For example, previous studies have pointed out that the proportion of people over 65 years old and young people under 25 years old using mobile payment is 1:11 (Li et al., 2020). As an important consumer power group, it is necessary to understand their perspectives on financial consumer technology aspects in order to promote today’s fintech-driven consumption and digital inclusion (Filho et al., 2019). However, in general, previous studies have shown that older adults are particularly hesitant and less willing to use new technology (Benson et al., 2019). Most existing studies focus on non-elderly people, while few studies focus on mobile payment adoption among the elderly (Cham et al., 2022; Hanif and Lallie, 2021), and mobile payment is not easily accepted by elderly users (Benson et al., 2019). Most of the previous studies on mobile payment classified consumers into a large group (Yang et al., 2023) without subdividing the population, which is obviously not conducive to the current realistic situation of population aging. Therefore, it is very necessary to understand the intention of the elderly to use mobile payment, which can help bridge the digital divide to a certain extent.

Theoretical basis

TAM

Davis et al. (1989) proposed the TAM as an explanatory framework for user adoption of technological systems. Behavioral intention, which represents the perceived extent to which users balance their reactions to technology and their own expectations when deciding to engage in a particular technological behavior (Faqih and Jaradat, 2015), has a direct on usage behavior (Davis et al., 1989). The model contains two core variables: perceived ease of use (PEOU) and perceived usefulness (PU), which are interrelated and influence users’ attitudes and intentions towards technology usage (Davis et al., 1989). The TAM is a well-validated theory extensive used for predicting user acceptance of technology (Hsu and Lu, 2004).

TAM is the most widely used and influential user technology behavior theory (Munoz-Leiva et al., 2017), which has been applied to mobile payment (Hameed et al., 2024; Wong et al., 2022), Electronic Banking (Afzal et al. 2024; Carranza et al., 2021) and assistive technologies for the elderly (Wei et al., 2023; Yang et al., 2023). For example, Wang and Dai (2020) adopted the TAM to explore the use of mobile payments by consumers in physical stores in China. They found that PU and PEOU affect users’ intention to adopt mobile payments. Mu and Lee (2021) discussed Chinese users’ intention on near field communication mobile payments using the TAM. Coskun et al. (2022) examined Turkish bank customers’ use of online payment systems during the COVID-19 pandemic using the TAM. They found that PEOU and PU affected user intentions. These studies suggest that TAM-based models can be used to explain older users’ perceptions of new technologies.

PEOU is defined as the level of minimal efforts recognized by users in using a particular technological system (Davis et al., 1989). When a technical is easy to use, it is more likely to be accepted by users (Davis et al., 1989). Sun and Chi (2018) demonstrated that PEOU has a positive impact on PU. Mu and Lee (2021) show that users’ perception of the ease of mobile payment in near field communication will directly affect their perception of usefulness. Arvidsson (2014) highlighted that PEOU is the most important factor affecting users’ acceptance of mobile payment. Tan et al. (2014) pointed out that perceived ease of use has a significant positive impact on users’ willingness to make mobile payments. In addition, Other studies (Davis et al., 1989; Sleiman et al., 2021) also indicated that PEOU directly affects users’ intention to use the technology, and indirectly affects PU, thereby influencing users’ behavioral intentions. Mozdzynski and Cellary (2022) study on mobile payment system found that e-merchants’ perception of the ease of use of mobile payment system had a significant positive impact on their usage intention. So, we propose that:

H1: PEOU was positive effect on PU.

H2: PEOU was positively affects the mobile payment intention (MP) of the elderly.

If consumers perceive that information technology can enhance their job performance without sacrificing usability, they are more likely to use it (Davis et al., 1989). PU is the degree to which a user believes that the adopt of a technical will enhance their work performance (Davis et al., 1989). It is the most critical predictor of users’ willingness to use mobile commerce (Faqih and Jaradat, 2015). In their study on the bank customers’ adoption of online payment systems in Turkey, Coskun et al. (2022) found that PEOU and PU affect users’ intentions. In the study of Kim et al. (2010), the adoption of mobile payment by Chinese users is not only influenced by perceived usability, but also significantly positive influenced by perceived usefulness. Mozdzynski and Cellary (2022) found that perception of e-merchants’ usefulness to mobile payment systems significantly affects users’ usage intention. Tan et al. (2014) pointed out that perceived usefulness has a significant positive impact on users’ willingness to make mobile payments. Mu and Lee (2021) studied the intention to use near field communication mobile payments, and found that PU affect users’ mobile payment intentions, while directly impacting their payment intentions. Therefore, we propose that:

H3: PU positively affects MP in the elderly.

ISSM

Aiming to explore the relevant criteria for information system success, ISSM contains three key dimensions, namely information quality (IQ), system quality (SYQ), and service quality (SEQ). The model predicts that technology systems can be evaluated according to their IQ, SYQ, and SEQ, which will significantly affect the user’s intention or actual use. It has been extensive use in studies on users’ adoption of information technology (Gao and Waechter, 2017), but the application of mobile payments is still limited. For example, Chen and Cheng (2009) explored users’ intention to shop online, Zhou (2011) studied users’ adoption of mobile websites, and Yuan et al. (2020) investigated users’ loyalty towards mobile payments. Based on the ISSM, Koghut and Ai-Tabbaa (2021) empirically-analyzed the inhibiting factors that affect consumers’ continuous use of mobile payment. In addition, Kuo (2020) and Zhong and Chen (2023) both extended the successful model of information system in the context of mobile payment. Therefore, under the background of population aging, this study attempts to expand the success model of information system as one of the theoretical bases.

Chi (2018) defined IQ as the completeness, relevance, and security of the content provided by the system. For example, in mobile payments, accurate information about transactions and amounts is provided to users. Gorla et al. (2010) proposed that IQ includes both information content and information format. Consumers always expect accurate mobile payment transaction history information. (Gao and Waechter, 2017). When the payment platform provides inaccurate or untimely information, consumers may doubt the platform’s capabilities, which could affect their trust (Gao and Waechter, 2017). Almaiah et al. (2022) suggested that the improvement of IQ, particularly in transaction histories, in mobile payments has replaced some paper payment receipts, which brings users different experience and trust. Several studies have demonstrated the significant impact of IQ on user trust. For example, Zhou (2011) found that poor IQ has a negative effect on consumers’ trust in mobile websites. Yang (2016) highlighted the positive role of IQ in users’ initial trust in mobile shopping. Researches by Wang et al. (2009) and Muhammad et al. (2014) both found the positive effect of IQ on user trust. Lee and Chung (2009) pointed out in their research on user behavior of mobile banking that IQ has a positive impact on users’ trust. In addition, Al Amin et al. (2023) confirmed the positive impact of IQ on the willingness to continue using mobile payment electronic systems. Almaiah et al. (2022) conducted a study on the adoption of mobile payment by near-field communication users in Saudi Arabia, and the results showed that IQ had a positive impact on usage intention. This study argues that the information provided by mobile payments, such as payment reminders, may be inaccurate and may involve risks for older users. Hence, it hypothesizes that higher IQ in mobile payments is likely to increase trust among older users. At the same time, IQ has a positive impact on the elderly’s mobile payment intentions. The hypothesis is proposed as follows:

H4: IQ positively affects PT.

H5: IQ positively affects MP in the elderly.

According to DeLone and McLean (2003), SYQ is defined as the technical quality of the overall system performance. In mobile commerce, a high-quality mobile system is a prerequisite for gaining user trust (Almaiah et al., 2022). When consumers perceive that the technology service provider’s system is of high quality, they hold a high degree of trust in it (McKnight et al. 2002). Yan and Pan (2015) believe that those low-quality mobile payment technology systems will reduce consumers’ evaluation of the reliability and credibility of mobile payment. Poor SYQ will discourage users from using mobile payment technology (Koghut and Ai-Tabbaa, 2021). Some previous studies on mobile payment services (Gao and Waechter, 2017; Zhou, 2014) prove that SYQ has an important impact on users’ willingness to mobile payment. Yang (2021) demonstrated the relationship between SYQ and users’ willingness to continue technological behavior. Al Amin et al. (2023) pointed out that SYQ would affect users’ intention to continue using mobile support during the COVID-19 pandemic. In addition, Vance et al. (2008) argued that SYQ in mobile commerce technology includes the system’s navigational structure and visual appeal, which significantly influence consumers’ trust in mobile commerce technology. Lee and Chung (2009) found that users’ perception of the quality of mobile banking systems affects their trust. Yuan et al. (2020) explored users’ loyalty towards mobile payments and indicated that SYQ has obvious significance on trust. Therefore, we proposed:

H6: SYQ significantly positively affects PT of elderly to use mobile payments.

H7: SYQ positively affects MP in the elderly.

According to Hsu et al. (2014), SEQ as the service support provided to consumers by a technical system or its provider. When consumers receive a particular technology or service, they require professional, reliable, and personalized support (Zhou, 2013). If the services provided by technology service providers are unreliable and slow in response, consumers cannot establish trust in their technological services (Gao and Waechter, 2017). Internet service providers with high SEQ are more likely to gain users’ trust (Quach et al., 2016). Poor SEQ diminishes users’ experience, raises doubts about the technology, and ultimately affects their trust (Gao and Waechter, 2017). In e-commerce, SEQ influences users’ trust in e-commerce (Kassim and Asiah Abdullah, 2010). Previous research on mobile services (Lee and Chung, 2009) indicated that SEQ affects users’ trust. Yang’s (2016) research on mobile shopping confirmed the positive impact of SEQ on users’ trust. In addition, Al Amin et al. (2023) confirmed the positive correlation between SEQ and the intention of continuous use of mobile payment electronic system. Yang et al. (2014) also pointed out that SEQ positively affects consumers’ intention to use mobile services. At the same time, in the context of e-learning, SEQ will have a positive impact on users’ intention of continuous use (Sharma et al., 2017). This study believes that the quality of service provided by mobile payment systems or applications may also be the concern of elderly users, including whether the service is professional and personalized. Therefore, we proposed:

H8: SEQ significantly positively affects PT of elderly to use mobile payments.

H9: SEQ positively affects MP in the elderly.

Additional variables

Perceived risk (PR)

PR is the potential losses that may incur to the users when using a particular technology (Lee and Song, 2013). Perceived risk, as one of the important barriers affecting users’ adoption of financial technology (Liébana-Cabanillas and Lara-Rubio, 2017), isa key factor influencing users’ adoption of mobile payments (Purwanto and Loisa, 2020). PR hinders customer adoption of payment platforms (Oliveira et al. 2016), and impedes users’ continued use of mobile payment technology systems (Yang et al., 2015). From the perspective of individual users, Harris et al. (2019) believe that risk and trust are the key factors affecting users’ use of payment systems. Natasia et al.’s (2021) study involving 415 Indonesian users found that PR has a negative effect on users’ intention to continue using payment services, regardless of gender. Pal et al. (2021) pointed out that PR is negatively correlated with users’ intention to use mobile payment. Liébana-Cabanillas et al. (2021) pointed out that users have concerns about privacy and data leakage when making mobile payments, and these possible risks reduce users’ intention to use technology (Flavián et al., 2022). The negative impact of PR on users’ technology adoption has been demonstrated in mobile payments (Liébana-Cabanillas and Lara-Rubio, 2017) and e-commerce (Herrero and San Martín, 2012). While the elderly prefer to have more control over their finances (Caffaro et al., 2018), mobile payment makes it easier for the elderly to perceive risks (Kim and Yang, 2016). Therefore, in the context of mobile payment, it is necessary to take the PR into consideration. Thus, we proposed:

H10: PR has a significant negative impact on the mobile payment intention of elderly users.

Perceived trust

Trust is an essential component in purchasing or engaging in technological transactions (Chong et al., 2018), reflecting consumers’ beliefs about the level of trust they have in transaction service providers (Zhou, 2014). It represents users’ perceived reliability of information provided by technology service providers (Mu and Lee, 2021). Typically, consumers of mobile payment are concerned about risks such as transaction errors and personal information privacy (Dahlberg et al., 2003). Consumers are reluctant to participate in online transactions because they lack trust in the process of online transactions (Bryce and Fraser, 2014). As an important factor for the success of online transactions in e-commerce and online banking (Yang et al., 2023), trust is also an influential factor for the success of mobile payment (Dutot, 2015). Qasim and Abu-Shanab (2016) point out that users’ trust will enhance their use of mobile payment, while reducing users’ sense of risk towards technology. When users have trust in technology systems, users’ willingness to continue using mobile payment services will be enhanced (McKnight et al., 2002). Zhou (2013) found that when suppliers trust mobile payment services, their willingness to use mobile payment will increase. In the context of mobile payment, trust has also been proved to be the key influencing factor of users’ mobile payment intention (Mu and Lee, 2021; Penney et al., 2021). When users perceive trust, the uncertainty of mobile payment and other factors will be reduced, thus increasing the proliferation of mobile payment services (Sharma and Sharma, 2019). So, we propose that:

H11: PT direct positive influence the intention of elderly to use mobile payment.

Social influence

SI means that users’ use of a technology is influenced by the views of important people around them (Venkatesh et al., 2003). This includes perceptions of technology adoption from friends, family, relatives, etc. (Baptista and Oliveira, 2015). These opinions have an impact on users’ mobile payment behavior (Alalwan et al., 2016), reflecting the influence of friends, family members, relatives, and others in the user’s social circle on their mobile payment behavior. Social impact is an extremely important construct in user intention studies of mobile applications (Chopdar et al., 2018). Many previous studies (Arar et al., 2021; Chen and Chan, 2014; Lee and Coughlin (2015) showed that older people’s social relationships, which include encouragement from family and friends, are of great significance for older people’s technology adoption. Soh et al. (2020) pointed out that SI is one of the factors affecting the elderly’s acceptance of technology. Wang and Dai (2020) demonstrated in their study on mobile payment that SI is a factor affecting users’ intention to adopt mobile payments. Al-Saedi et al. (2020) revealed the positive impact of SI on consumers’ intention to use mobile payment systems. Coskun et al. (2022) reveals that SI is direct influence on the use of online payment system by Turkish bank customers. In the study of Malarvizhi et al. (2022) on mobile payment by near field communication, SI is proved to have a significant impact on users’ adoption intention. Kim et al. (2024) confirmed that the intention of users aged 20–70 to use assistive technology development platforms is influenced by society. However, the role of SI has not reached a consensus and requires further validation. In some previous payment usage studies (Almaiah et al., 2022; Lisana, 2021, 2022), SI has been confirmed as a prerequisite factor for users’ payment behavior intention. However, this conclusion has not been supported in some previous studies (Moorthy et al., 2020; Zhou et al., 2021). Besides, previous studies have shown that the number of minors in the family is positively correlated with the intention of the elderly to use Internet technology (Cáceres and Chaparro, 2019), which indicates that minors may guide the adoption of technology by the elderly through explanation and demonstration (Neves and Amaro, 2012). This also suggests that older people’s technology use may be influenced by loved ones. However, there are also studies (Chesley, 2006) that show that the presence of family members and children cannot clearly explain the use of Internet technology, and some family members’ help may even directly replace technology, which has a negative impact on the adoption of technology by the elderly in the family (Demiris et al., 2008; van Hoof et al., 2011). These inconsistencies indicate the need for further research and verification. As a member of society, individual users cannot ignore the influence of society on their choices (Hameed et al., 2024), especially for the elderly who are not familiar with new technologies. Therefore, whether the willingness of elderly to use mobile payment is influenced by social influence variables requires further verification. This study believes that the more support and encouragement the elderly receive from their families, relatives and friends, the more likely they are to accept and use mobile payment. So, we propose that:

H12: SI has a direct positive influence on MP in the elderly.

Our study presents a model of elderly’s acceptance of mobile payment (Fig. 1).

Fig. 1: Conceptual Model.
figure 1

This model of mobile payment acceptance for the elderly provides a theoretical basis for the study of mobile payment behavior of the elderly.

Full size image

Methods

Sample

The respondents of this study are Chinese elderly people aged 60 and above, who are able to travel independently and have mobile payment experience. The interviewees were all from Zhanjiang, Guangdong Province, China. As the largest economic province in China, Guangdong Province also has the largest permanent resident population in China. According to the latest bulletin of the 7th National Population Census of Guangdong Province, as of November 1, 2020, the population aged 60 and above accounted for 12.35% of the permanent population of Guangdong Province, and Zhanjiang, as the fifth prefecture-level city with the number of permanent residents in Guangdong Province (there are 21 prefecture-level cities in the province). The proportion of the elderly aged 60 and above is 16.79% (Statistics Bureau of Guangdong Province, & Guangdong seventh national population Census Leading Group office, 2021), these elderly people have a certain representation. A total of 316 valid data were retrieved, which meets the recommendation of Kerlinger (1966) that the sample size in a structural equation modeling context should be at least 10 times the number of variables. It also satisfies previous studies (Barclay et al., 1995; Gefen et al., 2000) suggested that the sample size should be ten times the number of measurements of the largest dimension in the research model. Among the 316 elderly participants, the majority are males (166, 52.5%), while there are 150 females (47.5%). Most of them are in the age range of 60–69 (164, 51.9%), 142 are in the age range of 70–79 (44.9%), and only 10 are 80 years old or above (3.2%). In terms of educational, 105 had elementary school education or below (33.2%), 117 had completed junior high school education (37%), 83 had completed high school education (26.3%), and 11 had college education or above (3.5%). In terms of mobile payment experience, the majority had 1–3 years of mobile payment experience (131, 41.5%), followed by less than one year of mobile payment experience (100, 31.6%), 4–6 years of mobile payment experience (77, 24.4%), and more than 6 years of mobile payment experience (8, 2.5%).

Instruments

The questionnaire design of this study is based on the reference to the previous literature, combined with the theme of this study to determine the measurement questions. The scale included nine constructs. The constructs of PU and PEOU were adapted from Davis (1989), while Those of IQ, SYQ, and SEQ were adapted from Zhou (2011). The PT construct was adapted from Jain et al. (2022). The construct of SI was adapted from Venkatesh et al. (2012). The PR of mobile payment were adapted from Habib and Hamadneh (2021). The measure of mobile payment intention was adapted from Maduku and Thusi (2023). Besides, the questionnaire included demographic characteristics : gender, age, educational level, and shopping mobile payment experience. After the questionnaire design was completed, two elderly individuals were invited to read the questionnaire and provide suggestions for modifying certain wording to enhance participants’ understanding.

Data analysis

In this study, SPSS 25 was used to examine the questionnaire data. Partial Least Squares Structural Equation Modeling (PLS-SEM) can be used to analyze complex models, and it has no hard and fast rules on the distribution of study data (Hair et al., 2017). Meanwhile, PLS-SEM can also be used in forecasting studies (Hair et al., 2014). Moreover, PLS-SEM has the ability to estimate more complex models using smaller sample sizes (Hair et al., 2019). This method has been widely used in many fields such as information science (Goršič et al., 2017), psychology (Sarstedt et al., 2020) and gerontology (Marsillas et al., 2017). Therefore, PLS-SEM is used for data statistics.

Common method variance (CMV)

CMV was considered and addressed. The reduction and evaluation of CMV were primarily achieved through the questionnaire design and subsequent data analysis. First, in the questionnaire design, the items were presented using language that was easy for participants to understand (Hew et al., 2017). Additionally, participants were encouraged to complete the questionnaire anonymously to minimize CMV. After data collection, based on Podsakoff et al. (2003), a Harman single factor test was conducted. The research revealed that the single factor with the largest variance explained 37.497% of the overall variance amount, which is below the 50% standard, suggesting that CMV is insignificant in this study (Lavuri et al., 2022; Tan et al. 2017).

Results

Measurement model

Chin (1998) pointed out that when the values of Cronbach’s alpha (CA) and composite reliability (CR) are higher than 0.7, and Average Variance Extracted (AVE) are higher than 0.5, the reliability meets the requirements. In this study, Cronbach’s alpha and CR for each construct exceed 0.7, while the minimum value of AVE is 0.659, indicating good reliability, as shown in Table 1. Carmines and Zeller (1979) believes that the load of each measurement questionnaire should be higher than 0.7 to pass the index reliability test. Table 1 shows that the load of the measurement index meets the standard.

Table 1 Analysis of measurement indicators.
Full size table

Hair et al. (2010) pointed out that AVE higher than 0.50 indicates that the model has convergence validity. The AVE values of all dimensions range from 0.659 to 0.834 (Table 1), which meets the relevant standards, indicating that the convergence validity meets the standards. This study also used the criteria proposed by Fornell and Larcker (1981) and the Heterotrait-Monotrait Ratio (HTMT) values (Henseler et al., 2015) to assess discriminant validity. Fornell and Larcker (1981) pointed out that when the square root of AVE value of constructs is higher than the value of phase relation between variable, it indicates that the questionnaire has differential validity. Table 2 shows that the AVE square root value on the diagonal is higher than the value of the phase relationship between constructs, indicating good discriminative validity. Moreover, the HTMT values below 0.85 indicate discriminant validity among the variate (Henseler et al., 2015). Table 3 shows that HTMT is below the standard of 0.85, indicating that the model has differential validity. In conclusion, the model exhibits satisfactory reliability and validity.

Table 2 Fornell-Larcker criterion.
Full size table
Table 3 HTMT.
Full size table

Structural model

The Standardized Root Mean Square Residual (SRMR) is one of the indexes to evaluate the goodness of fit of a model (Henseler et al., 2016). When SRMR is less than 0.08, it indicates that the model has a good fit (Hu and Bentler, 1998). The results of this research show an SRMR value of 0.065, confirming to be a good model fit. Additionally, Tenenhaus et al. (2005) suggested that the Goodness of Fit (GOF) is an effective indicator for evaluating model fit, and when the GOF above 0.36, the model has a high fit. The computed GOF in this study is 0.554, which met the high fit standard. In summary, both the standardized SRMR and GOF indicate a high model fit.

The coefficient of determination, R2, represents the variance explained by exogenous variables (Kline, 2023). Keil et al. (2000) point out that R2 is the indicator of the explanatory force of the model. Typically, R2 values of 0.75, 0.50, and 0.25 represent substantial, moderate, and weak, respectively (Hair et al., 2011). However, the interpretation of R2 values depends on the discipline and research context. An R2 as low as 0.10 can be considered acceptable (Hair et al. 2019). In Table 4, the coefficients of determination R2 values for mobile payment intention, PT, and PU in this study are 0.590, 0.364, and 0.272, respectively, it means that the model has explanatory power.

Table 4 Coefficients of determination.
Full size table

Figure 2 and Table 5 show the evaluation results. The result indicate that PEOU (β = 0.522, p < 0.001) positively influences PU. PU (β = 0.158, p < 0.01) and PEOU (β = 0.262, p < 0.001) have a directly influence on elderly’s MP, supporting H1, H2, and H3. IQ (β = 0.454, p < 0.001) and SEQ (β = 0.231, p < 0.001) have a directly influence on PT, confirming H4 and H6. Additionally, IQ (β = 0.103, p < 0.05) and SEQ (β = 0.156, p < 0.001) also have a directly influence on mobile payment intention, supporting H5 and H7. However, SYQ has insignificant impact on PT (β = −0.023, p = 0.669) and MP (β = −0.044, p = 0.226), thus H8 and H9 are not supported. PT (β = 0.262, p < 0.001) is identified as a positive antecedent of mobile payment intention, supporting H10. Social influence (β = 0.111, p = 0.129) had no significant influence on MP, thus H11 is not supported. Besides, PR (β = −0.148, p < 0.001) is also proved to have a negative impact on MP, validating H12.

Fig. 2: Assessment result.
figure 2

This study revealed the influencing factors of the elderly’s mobile payment intention.

Full size image
Table 5 Hypothesis test results.
Full size table

Discussion

With the popularity of smart phones and communication technologies, mobile payment has become a crucial financial service technology for users (Malarvizhi et al., 2022). In an attempt to uncover the factors that influence older people’s intention to use mobile payments, our research established a mobile payment acceptance model for the aged based on the TAM and the ISSM. The model includes nine variables: perceived ease to use, PU, IQ, SEQ, SYQ, PT, PR, SI, and mobile payment intention. This study confirmed the important factors influencing the elderly’s mobile payment behavior. The factors in the model explain 59% of the variance, shaping the elderly’s MP when shopping.

Our research results demonstrate that PT is the key factor influencing elderly’s use of mobile payments. This result is consistent with Mu and Lee (2021), indicating that when the elderly have confidence in the security and privacy of mobile payment transactions, the elderly’s trust in mobile payment will increase, and its adoption intention will be higher. The trust of the elderly in mobile payment, and their trust in the information provided by the payment system during the payment process, will have a positive impact on the MP by the elderly. Given the vast market of elderly consumers, it is crucial to establish trust among them regarding mobile payment. Users need to build trust in order to increase their use of technology. Just as Kim et al. (2010) conducted a study on mobile application user behavior, it is confirmed that users’ trust in technology will play an impact on their willingness to use technical services. Therefore, efforts should be made to enhance the SEQ, security, reliability, and privacy protection of mobile payment systems or platforms to enhance the trust of older users and decrease their PR associated with mobile payment, ultimately enhancing the MP (Qasim and Abu-Shanab, 2016).

The results demonstrate that factors influencing the elderly’s adoption of MP include PR, which has an inverse impact on their use of mobile payments. This finding is consistent with the results of Natasia et al. (2021) and Liébana-Cabanillas et al. (2021), which indicate that elderly people’s concerns about factors or security risks caused by the use of mobile payments reduce their intention to use mobile payments. However, the results of this study are inconsistent with the results of Malarvizhi et al. (2022), which believes that the negative impact of PR on users’ intention to use mobile payment is not significant. The inconsistency may be caused by the fact that the research objects of Malarvizhi et al. (2022) are not elderly people. They are more familiar with and highly dependent on mobile payment, and they are less concerned about its risks (Malarvizhi et al., 2022), while the objects of our study are elderly people. They are relatively unfamiliar with the new technology of mobile payment, and they are more concerned about property resources, which makes the risk awareness more strong. Therefore, Companies that develop mobile payment platforms should take careful risk control measures, by mitigating the risks associated with mobile payment, such as payment system risks, financial risks, and personal information risks, positive effects can be generated on the elderly’s acceptance of MP, enhancing their intention to use it. Furthermore, as individuals are part of a social group, they frequently interact with their social networks, and their moods and behaviors can be easily influenced by the people and things around them. Luan et al. (2006) pointed out that when a technological innovation becomes a consumer trend, users are mostly influenced by their friends and family. However, our results indicate that SI does not affect the elderly’s use of MP. This is consistent with the results of Moorthy et al. (2020), which shows that elderly users’ use of mobile payment is less influenced by the views of people around them. This result may be due to the fact that the payment transaction involves the capital property and important personal information of the elderly, so they are more rational and prudent, more assertive, and not easily influenced by people around them. However, the insignificant influence of SI on mobile payment intention in this study contradicts the findings of Wei et al. (2023). The reason for this may be that Wei et al. (2023)’s study only focuses on the influence of family children who are closely related in social relations. While this study focused on children’s guidance and support for technology in older adults, ours is a broader concept of social impact, rather than focusing on the social structure of an older person.

This study found that the SEQ of mobile payment positively affects users’ trust, which is consistent with Kassim and Asiah Abdullah (2010). This indicates that the first step to eliminate user resistance to new technologies is to build user trust in the technology (Laksamana et al., 2023). Consumers’ evaluation of the SEQ of technological systems often takes time but typically occurs during their continuous interactions with the mobile payment system (Gao and Waechter, 2017). SEQ reflects the characteristics of system reliability, responsiveness, assurance, and personalization (Gao and Waechter, 2017). Since mobile payment involves users’ personal information and financial security, mobile payment providers need to enhance users’ trust by offering professional, reliable, and personalized services. Additionally, given the diverse range of mobile payment platforms, with their main functional modules being similar, providing personalized services tailored to the elderly user group may more easily gain the favor of older users and enhance their service experience. Our research also shows that SEQ has a significant impact on usage intentions, which calls for the importance of aging user-centered design concepts (Fanning et al., 2024). Work with target elderly users to design mobile payment technology-related service content that meets the individual needs of the elderly, including interactive interface, operation mode, visual style, function expansion, information content, etc. Through the form of co-creation, users can realize the easy-to-use and easy-to-use recognition of technical products or services, improve the SEQ of mobile payment, and ultimately promote the adoption of mobile payment. At the same time, marketers related to mobile payment platforms should do a good job in front-end service work. And before that, receive training on how to communicate with elderly consumers about mobile payment technology, understand the factors that help and hinder the use of technology by the elderly, such as trust and risk, in order to improve the quality of mobile technology related services, reduce the concerns and encouragement of elderly users about mobile payment, enhance trust, and promote their adoption of mobile payment. In addition, The quality of information reflects the relevance, adequacy, accuracy, and timeliness of the information provided by mobile payment systems. Consumers may have doubts in the platform if they find the information provided by the platform inaccurately or outdated (Gao and Waechter, 2017), this will likely affect consumer trust. Lee and Chung (2009)’s research on mobile banking confirmed the positive impact of IQ on users’ trust, which was also validated in our research. Consumers generally desire access to their past payment information anytime and anywhere (Gao and Waechter, 2017). At the same time, information quality has a positive impact on mobile payment intentions of older users, which was also confirmed in a previous study in Africa (Franque et al., 2021). This shows the importance of mobile payment systems to provide users with accurate and timely information. Therefore, as a mobile payment provider, the mobile payment platform should timely and accurately provide transaction information to customers based on their actual needs, satisfying users’ information requirements and perception of quality, improve the user experience and enhance the close relationship between users.

Furthermore, our results show that PEOU directly influences users’ mobile payment intention. This is the same as the previous study (Coskun et al., 2022). Our research supports previous studies that suggest a close correlation between older adults’ perception of technology usability and their acceptance of technology (Dogruel et al., 2015). It indicating that when elderly users can easily complete payment transactions, it positively influences their adoption intention. This also indicates that elderly people believe that they still need to understand the use of new technologies. When elderly people are familiar with mobile payment technology, they will feel to some extent that the use of technology is not so difficult. These results further demonstrate the importance of improving the simplicity and ease of use of mobile payments to meet the needs of elderly users. Therefore, mobile technology developers need to design and develop for different groups such as the elderly (Glynn et al., 2012). For example, speech speed, interface, interface layout, and operation key size with age appropriate features can be designed based on the cognitive behavior of the elderly (Zhou et al., 2022), and mobile payment programs with friendly payment interfaces can be developed to enhance users’ recognition of the ease of use of mobile payments. Additionally, PU significantly affects elderly’s MP. When elderly users engage in shopping activities, the use of mobile payment systems saves their time and effort, thereby increasing shopping efficiency. This ensures the users’ utilization of mobile payment, a result validated in our research and supported by previous study (Coskun et al., 2022; Mu and Lee, 2021). This suggests that older adults are more likely to adopt mobile payments when they perceive them to be useful and contribute to improving their quality of life (Moxley et al., 2022). In the digital age, simplicity, efficiency, less inconvenience, and security are useful (Gobble, 2018), and the same applies to mobile payments. Therefore, it is necessary to provide services or products with these characteristics to enhance users’ perception of the usefulness of technology, and thus enhance users’ intention to use technology.

Conclusions, implications, and limitations

Conclusions

Mobile payment, as a novel transactional payment method, has gained attention among users. However, in the context of population aging, few studies have focused on elderly people’s willingness to use mobile payments (Cham et al., 2022; Hanif and Lallie, 2021). This study integrated the TAM and ISSM to develop a model for elderly’s acceptance of mobile payments. Partial least squares structural equation modeling was used to demonstrate the model. Research shows that the elderly shopping mobile payment acceptance model has 59% explanatory power, and the Goodness of Fit of the model reaches a high matching. This shows that the model has high explanatory power and fit, and the model is effective. It was found that PEOU is positively correlated with PU. Additionally, IQ and SEQ positively influence PT. Moreover, the mobile payment intention of the elderly is directly and positively influenced by PU, PEOU, IQ, and SEQ. and is negatively affected by PR. Perceived ease to use is the front influencing factor of Perceived usefulness, and PT is directly affected by Information quality and SEQ. In addition, the impact of SYQ on Mobile payment intention and PT of the elderly is not significant. The Mobile payment intention of the elderly is not affected by SI. This study revealed the influencing factors of the elderly’s mobile payment intention, further expanded the application of TAM and ISSM, and enriched the behavioral theory of elderly mobile payment users. The research findings can provide valuable insights for mobile payment technology developers, designers, and policymakers, facilitating age-appropriate design of mobile payment technology platforms and holding both theoretical and practical significance.

Theoretical and practical implications

Our research has some academic value. First, on the basis of integrating the TAM and the ISSM, this study adds additional variables and proposes and validates the model of the elderly’s shopping mobile payment intention. It has significant explanatory power, accounting for 59% of the variance in the elderly’s MP. Hence, it provides a theoretical model for the research of technology acceptance. Second, the factors identified in our research, including the SEQ of the technology system, IQ, and personal perception-related factors such as PT and PR, enriched the understanding of the elderly’s mobile payment behavior. Third, this study extended the application of the TAM and ISSM to the field of technology and elderly user groups on mobile payments. Lastly, our research contributes to the literature on user intention to adopt mobile payment, particularly in the older age group, thus providing insights for research on elderly behavior in other domains.

Furthermore, our research has some practical value. First, First, this study reveals the positive effects of IQ and SEQ of mobile payment systems on the elderly’s mobile payment intention. Therefore, when designing and developing payment systems, designers should create systems that provide high-quality information and services to users, aiming to improve the mobile payment experience for elderly users. This can be achieved by focusing on providing comprehensive product information and reliable responsiveness, thus enhancing the mobile payment technology system. Second, the PT and PR of elderly users influence their intention to use mobile payment. Hence, mobile payment developers should strive to reduce risks to increase user trust, instead of solely focusing on obtaining users’ personal data and advertising revenue without considering the implications of mandatory authorization and advertising push notifications for various privacy information. Third, the results of this study, besides being relevant to mobile payment developers, can also serve as a reference for policymakers and regulatory agencies involved in formulating management measures for mobile payment applications and network supervision policies.

Limitations and follow-up exploration

There are also some limitations in our research, which can be attempted to address in subsequent studies. First, the objects of this study are Chinese elderly people, and fewer of them are 80 years old or above and have college education or above. This is a regional exploratory study, just as Sleiman et al. (2022), Fan et al. (2024), and An et al. (2024) also explored users in a certain country or region, which has certain value. However, This undoubtedly limits the generalizability of the research results to users in countries with different cultural backgrounds and users in regions with higher levels of education, particularly in developed countries. After all, the preferences and needs of the elderly may be slightly different depending on cultural background (Fanning et al., 2024). As Flavian et al. (2020) points out, factors such as culture or diversity of different countries may affect consumers’ intention to use mobile payment, which requires further rigorous exploration. Therefore, future research could enrich the study sample and its structure, such as by conducting cross-national or cross-regional studies, and even carry out comparative studies of rural and urban elderly people. Second, in the era of rapid digital economy development, the factors influencing user behavior are diverse. Therefore, the subsequent research should include more potential factors to explore on the basis of culture, so as to enrich the research results. For example, considering the differences and emphasis of collectivism, individualism and family bonds in different countries or regions, the subsequent research can integrate culture and explore the influence of collectivism, individualism or social relations in detail, such as the research on friends, family members and relatives in social relations. For example, intergenerational relations (Zheng et al., 2023). Lastly, our research is cross-sectional, however, users’ behavioral intentions may vary with their experience or over time. Therefore, in the future, it is necessary to conduct comprehensive research to deeply reveal the mobile payment intention and explore the factors caused by the differences in their behavioral intention, which is worth looking forward to. This is an area of research that holds promise for further investigation.