With the increasingly widespread popularity of mobile devices, our daily lives have significantly changed, especially in terms of financial transactions. Mobile payment (M-payment) has been dramatically adopted in various industries in recent years. According to a WorldPay report, M-payments accounted for 22% of the global points of sale spending in 2019, and this percentage will increase to 29.6% in 2023 [1
]. Moreover, China’s overwhelming adoption of M-payments (Alipay and Wechat Pay) at the point of sale by using Quick Response (QR) codes drove nearly half (48%) of the point-of-sale payments in 2019 [1
]. Various previous studies have facilitated the understanding of adoption intentions of M-payment in different contexts [2
]. However, there are still deficiencies of determinant variation and theoretical evidence of different perspectives in emergency conditions [5
The 2019 novel coronavirus (COVID-2019) broke out in December of 2019 and has dramatically expanded globally. As of 7 December 2020, there were 66,243,918 confirmed cases of COVID-19 and 1,528,984 deaths worldwide, reported by the World Health Organization [6
]. Due to the high risk of COVID-19 transmission, reducing contact among people and maintaining social distancing was highly recommended by the WHO (2020b) and Tang et al. (2020) [7
]. In this sense, the contactless characteristic of M-payments can potentially contribute to users’ mental and physical expectations to support their transaction processes and protect their safety. Accordingly, adoption of M-payment in China has significantly increased during the COVID-19 pandemic. According to a report from China banking and insurance news (2020), during the COVID-19 pandemic, the number of transactions made by M-payment was 22.4 million in the first quarter of 2020 in China, up 187% from the previous year (2019) [9
]. Meanwhile, based on the CNNIC (2020) report comparing the smartphone users who used M-payment from 2019 to 2020, this percentage increased from 73.5% in June 2019 to 85.3% in March 2020 and reached 86.0% in June 2020 in China, which indicates that M-payment contributes to maintaining individual and organizational transactions during emergency situations [10
]. Furthermore, users’ payment habits and business models have changed from traditional face-to-face transactions to contactless M-payment transactions during the pandemic, which in turn efficiently supports the survival of various business and maintains the development of the social economy under an emergency situation. Therefore, what factors influence users’ intentions to adopt M-payment during the pandemic? It becomes dramatically valuable to understand customers’ behaviors under the pandemic for relevant researchers and stakeholders to comprehensively investigate information technology adoption under an emergency situation to develop business strategies correspondingly.
Traditional adoption models (e.g., Technology Acceptance Model (TAM) and the Unified Theories of Acceptance and Use of Technology (UTAUT)) evaluate users’ intentions determined by technological perceptions with an obvious limitation of influence from users’ mental perceptions [11
]. Notably, based on the recommendations of governments and the WHO (2020b) regarding restrictions of direct and indirect contacts among people under the pandemic situation [7
], the contactless feature of M-payment potentially influenced users’ attitudes regarding the benefits of using M-payment for daily transaction, which indicates that environmental conditions affect users’ mental process with regard to adopting M-payment [13
]. Meanwhile, due to the influence of the pandemic, perceived security and trust are also considered as additional antecedents of users’ adoption intentions of M-payment [15
]. Perceived security is the most significant determinant of trust, positively affecting users’ intentions of using M-payment [16
]. Therefore, this study proposes a new adoption model, including perceived benefits, performance expectancy, effort expectancy, social influence, perceived security, trust and behavioral intention, to investigate users adopting M-payment during the COVID-19 pandemic in the following sections: Section 2
: theoretical backgrounds of the utilization of M-payments during the COVID-19 pandemic, MAT and UTAUT; Section 3
: development of hypotheses and research model; Section 4
: research methodology and data demonstration; Section 5
: data analysis; Section 6
: discussion; Section 7
: theoretical and practical implications; Section 8
: limitations and future research recommendations; Section 9
5. Data Analysis
The covariance-based structural equation modelling (CBSEM) technique was conducted for quantitative data analysis. SPSS 17 and AMOS 22 were applied in this study, through the two-step approach suggested by Anderson and Gerbing (1988), including validating the measurement model and testing structural model. The maximum likelihood estimation was conducted in the model assessment [51
5.1. Measurement Model
A measurement model aims to assess fitness between indicators and latent variables. Exploratory factor analysis (EFA) was applied to examine the construct reliability, and a standard method factor analysis, confirmatory factor analysis (CFA), was applied to assess the convergent and discriminant validity of the measurement model. All seven hypothesized latent constructs in the CFA model were allowed to covary and were determined by related measurement items as reflective indicators.
Construct reliability was tested by Cronbach’s alpha. As presented in Table 3
, all Cronbach’s alpha values of latent variables are in the range of 0.807 to 0.897, all exceeding the 0.70 suggested by Nunnally and Bernstein (1994) [52
], which means that construct reliability has been demonstrated.
Convergent validity was assessed by standardized factor loading of all sample items. Table 3
shows that all items loadings are in the range of 0.807 to 0. All loadings are ideally greater than 0.70 [53
], which demonstrates eligible convergent validity of the measurement model. Moreover, completed convergent validity was assessed by Composite Reliability (CR) and the average variance extracted (AVE) criteria. As shown in Table 4
, the constructs have CRs in the range 0.811 to 0.898, all above 0.7 [54
]. Meanwhile, all constructs have AVEs in a range of 0.589 to 0.688, which all meet the suggestions by Fornell and Larcker (1981) (that AVE should be higher than 0.5) [55
], which means the latent variables explain more than half of the variance of the indicators. Therefore, the consistency of measurements among the indicators and latent variables has been proved.
Discriminant validity reflects whether two factors are statistically different. It is evaluated by using two criteria. Firstly, according to Fornell and Larcker (1981), the square root of the AVE should be greater than all correlations between each pair of constructs [55
]. Table 4
shows that for each factor the square root of AVEs is larger than its correlation coefficients between all latent constructs, which proves that each construct shared more variance with its associated indicators than with any other construct [55
]. Secondly, all AVEs are greater than the maximum shared squared variance (MSV) [56
]. Thus, the scales satisfy the criterion of discriminant validity suggested.
Meanwhile, the following criteria (the ratio of chi-square to degrees of freedom (X2
/df) < 3, comparative fit index (CFI) > 0.9, goodness of fit index (GFI) > 0.9, adjusted goodness of fit index (AGFI) > 0.9, normalized fit index (NFI) > 0.9, Tucker–Lewis index (TLI) > 0.9, and root mean square error of approximation (RMSEA) < 0.05) were applied to evaluate the fitness of the model. Table 5
shows that all the model-fit indices of the measurement model (X2
/df = 1.832, CFI = 0.979, GFI = 0.948, AGFI = 0.935 NFI = 0.959, TLI = 0.979, RMSEA = 0.034), respectively, exceeded the common acceptance levels, which demonstrates a qualified fitness of the measurement model.
Further, this study examined the potential common method bias by Harman’s one-factor test (Podsakoff et al. (2003)) in SPSS [57
]. The results show that the largest variance explained by an individual factor is 40.99% (<50%). The result confirms that none of the factors can individually explain the majority of the variance. Moreover, a CFA was applied to assess the fitness of a single-factor model (all items as the indicators of one factor) [58
]. The results of the model fit present a poor fitness, which include v2
/df = 15.999(>3), CFI = 0.603 (<0.9), GFI = 0.566 (<0.9), AGFI = 0.485 (<0.9), NFI = 0.588 (<0.9), TLI = 0.569 (<0.9) and RMSEA = 0.143 (>0.08). Therefore, both tests confirm that no common method bias appeared in this study.
The assessment results of the measurement model validate the construct reliability and convergent and discriminant validity of constructs satisfactorily. The constructs can be used to test the structural model.
5.2. Structural Model
The maximum likelihood estimation method and bootstrapping technique (500 samples and 95% significance level) were applied for assessing the structural model. Firstly, the model fit of the structural model was evaluated similarly to the measurement model. The results were presented in Table 5
, which confirms a qualified goodness of fit of structural model.
Secondly, the variance (R2
) of endogenous variables was assessed to evaluate the explanatory power of the structural model. As shown in Table 6
and Figure 2
, the explained variances of performance expectancy (R2
= 0.48), perceived benefits (R2
= 0.28), trust (R2
= 0.44) and behavioral intention (R2
= 0.71) all exceed the recommended minimum value, 0.1 [59
]. Thus, the structural model substantially explains the dependent variable.
Moreover, the results of hypotheses testing show that behavioral intention of adopting M-payments during the COVID-19 pandemic is the most significantly determined by performance expectancy (ß = 0.426; p < 0.001), followed by, perceived benefits (ß = 0.283; p < 0.001), social influence (ß = 0.277; p < 0.001), trust (ß = 0.234; p < 0.001) and perceived security (ß = 0.221; p < 0.001). Thus, Hypotheses H1, H2, H5, H7 and H10 are validated, respectively. However, the results show that effort expectancy (ß = −0.209; p < 0.001) has a negative effect on behavioral intention; therefore, H3 was rejected. Moreover, both effort expectancy (ß = 0.470; p < 0.001) and trust (ß = 0.401; p < 0.001) are statistically significant in terms of explaining the performance expectancy. Thus, Hypotheses H4 and H8 are confirmed. Meanwhile, the results illustrate that the perceived benefits are significantly influenced by social influence (ß = 0.272, p < 0.001) and trust (ß = 0.233, p < 0.001), respectively. Therefore, Hypotheses H6 and H9 are confirmed. In addition, H11 is also accepted by the result of perceived security (ß = 0.586; p < 0.001), significantly determining trust.
Based on the data analysis results, ten of the eleven hypotheses were confirmed in this study, which demonstrates that the current study exhibits an appropriate adoption model to explain antecedents of users’ adoption intentions of M-payment under the pandemic.
Specifically, performance expectancy had the most significant positive impact on users’ adoption intentions of M-payments during the COVID-19 pandemic (Hypothesis 2), which corresponds to the vast majority of previous studies [37
]. It can be confirmed that the utility and practicability of M-payment technology can improve users’ payment efficiency under emergency situations. Especially, M-payment provided a fast payment process without any direct or indirect contacts among people, significantly influencing users’ adoption intentions during the pandemic. Users will feel M-payment is a more useful and more reliable method than traditional payments to support their transactions under the pandemic.
Meanwhile, performance expectancy is significantly determined by effort expectancy (Hypothesis 4) and trust (Hypothesis 8), which is in accordance with findings from previous studies [2
]. This study initially validated the effects of effort expectancy and trust on performance expectancy under the pandemic, which explains the absence of a confirmation of the simplicity and trustworthiness influencing the perceived functional utility when using M-payment under an emergency situation. Accordingly, the results support the accessibility and operability of the technology’s interface and function are positively formulate users’ performance expectancy; meanwhile, the reliability and trustworthiness of the technology’s services are essential to shape the high utilization of the technology under an emergency situation.
Moreover, the second largest significant effect on users’ behavioral intentions to adopt M-payments during the COVID-19 pandemic is caused by perceived benefit (Hypothesis 1). This result illustrates that perceived benefits correspond with individuals’ mental expectations related to contributions of M-payment under the pandemic. Specifically, perceived benefits, such as M-payment’s efficiency, not only influence users’ perceived technological perceptions, convenience and utility [14
], but also increase perceived safety benefits by M-payment’s contactless characteristic. Concretely, users’ mental expectations are satisfied by perceiving more reliability and safety of using contactless payment to reduces contacts among people and maintains social distancing to decrease the COVID-19 transmission risk [7
]. Thus, perceived benefits reflect users’ mental cognitions of technology’s features which can overcome a particular environmental issue, which in turn significantly influences users’ adoption intentions.
Meanwhile, under the condition of COVID-19 pandemic, perceived benefits as mental expectations are significantly influenced by social influence (Hypothesis 6) and trust (Hypothesis 9). The effects of social pressure and opinions of important, relevant people play an important role in influencing an individual’s mental expectations, affecting his/her behavioral intention [14
]. When users receive recommendations from their close friends or families indicating that M-payment is beneficial for protecting their personal safety by avoiding contact with people during a transaction process to reduce the infection risk of COVID-19, they tend to consider M-payment as a helpful and valuable payment method. Moreover, trust was analyzed and found to have a significant effect on perceived benefits in this study. The reputation and trustworthiness of M-payments are potentially determined by the contactless advantage of M-payment in optimizing users’ experiences and supporting their safety during the COVID-19 pandemic, which emphasizes users’ perceived benefits towards adopting M-payments during the emergency situation.
Furthermore, social influence as the third important factor has a statistically significant impact on behavioral intention (Hypothesis 5), which means the opinions, recommendations and support from close relationships of users are essential in the formulation of users’ behavioral intentions to adopt M-payments during the COVID-19 pandemic. This result is supported by previous studies in normal situations [33
]. Especially under the pandemic, people are relying more on the support and recommendations of important people in their lives—their family and close friends more easily influence their behaviors. Accordingly, the reputation of M-payment and word-of-mouth effect are considered crucial for attracting users’ adoption intentions of M-payment to formulate a new payment habit by the influence of the pandemic.
In addition, this study confirms Hypothesis 7 and Hypothesis 10—trust and perceived security have statistically significant effects on explaining users’ behavioral intentions of using M-payments during the COVID-19 pandemic. Specifically, consumers have developed trust in M-payment platforms through their reliable performance and mature legal framework protection, and so they worry less about financial risks to reap more benefits from the service [39
]. Thereby, users’ adoption intentions are influenced by technological and privacy security and users’ trust from technological and mental perspectives [16
]. Moreover, Hypothesis 11 also proved that perceived security significantly associates with trust. In this sense, perceptions of users’ perceived security could reduce uncertainty as well as crucially guaranteeing the M-payment performance to improve users’ trust of M-payment platforms [15
]. It demonstrates that trust and perceived security have a significant association, and both factors conjointly determine users’ adoption intentions of M-payments under the pandemic. Furthermore, M-payments involve sensitive and personal data; therefore, it is necessary to ensure the reliability and credibility of M-payment platforms for securing transactions and protecting personal information [61
]. Moreover, based on the security, trustworthiness and reliability of M-payment platforms, users can accept the records of their transaction times and locations during the pandemic to be utilized by governments and health institutions to track contacts among payment processes, for monitoring, updating and reporting the pandemic transmission status. Accordingly, users can clearly and opportunely be made aware of the virus infection situation among them, which positively influences their intentions to use M-payment during the COVID-19 pandemic to reduce the infection risk.
However, Hypothesis 3 was rejected in this study, which means easiness of understanding and handling M-payment systems does not have a direct impact on a user’s behavioral intentions to adopt M-payments during the COVID-19 pandemic. Similar results are supported by previous M-payment studies [3
]. The main reason for this result is because users have become accustomed to smartphone functions and become more skillful through their previous utilization of various applications on smartphones [60
]. Meanwhile, under the COVID-19 pandemic, user behavior is determined more by other perceptions related to personal safety, such as reliability, utility, security, trustworthiness and benefits, which can provide multidimensional supports for protecting transaction processes during a pandemic. Thus, the easiness of using M-payment is a less critical or surmountable factor determining users’ adoption intentions during the pandemic.
7. Theoretical and Practical Implications
7.1. Theoretical Implications
This study contributes three main theoretical implications. First, this study was empirical and examined the factors affecting users’ adoption intentions of M-payments under the pandemic situation, which is absent from evaluations of previous studies. Consequently, the study dramatically enriched the literature of technology adoption during a pandemic. Specifically, this study illustrates a worthwhile direction to understand users’ adoption intentions by not only examining users’ perceptions from technological perspectives, but also assessing users’ mental expectations. Moreover, users’ technological and mental perceptions of technology are significantly influenced by the emergency situations. Therefore, this study provides a future sight for relevant research to analyze new technology adoption from technological and mental perspectives conjointly and corresponding with the specific situation, especially for emergency situations.
Second, this study integrated the UTAUT model with perceived benefits from MAT and two extra variables, perceived security and trust, which significantly contributes to the theoretical development and framework coordination of the emerging literature on information technology adoption. Simultaneously, this study demonstrates a substantial contribution to the theoretical expansion of UTAUT and MAT by initially proposing and verifying new causal paths (PB → BI, SI → PB, TR → PB, TR → PE, TR → BI and PS → BI) and rejecting the path EE → BI for investigating the interactions of variables in the new comprehensive model. Therefore, the integrative research approach presented in this study can serve as a beneficial and valuable reference to modify and evaluate new adoption models for investigating novel technology adoption.
Third, this study initially focused on technology characteristics corresponding to the pandemic situation as a potential antecedent determining users’ mental and technological perceptions. Specifically, the contactless feature of M-payment avoids contacts during transaction processes and maintains social distancing, which improves the perceived multidimensional benefits of the users and optimizes their experience of using M-payments under the pandemic situation. Meanwhile, based on the disaster status of the COVID-19 pandemic, the effort expectancy became less important than other variables for determining whether users would adopt M-payment. Thus, it is important to consider whether a particular technology’s features can influence users’ interpretations of the perceived mental and technological benefits corresponding to particular situations or conditions to comprehensively explain technology adoption in an emergency situation.
7.2. Practical Implications
Moreover, four main practical implications are demonstrated in this study. First, the current research enhances the existing knowledge of adoption intention of M-payments in an emergency situation and enriches the understanding of how a pandemic changes users’ payment habits. It suggests that a pandemic might bring suffering to people or society. Furthermore, it can also facilitate the development of new technology that can bring benefits to individuals, organizations and society to survive in the emergency situation, which is valuable for relevant stakeholders to consider the pandemic to establish appropriate business strategies.
Second, this study could be valuable to start-up companies, policymakers, government bodies and private service providers who are interested in M-payment services. M-payment has become increasingly popular and provides useful services for efficient transaction processes, particularly in emergency conditions. In the context of a pandemic, M-payment can increase personal safety perception and maintain the stable development of business. Based on the finding from this study, as well as providing an easy-to-use operating application, relevant stakeholders should initially recognize the importance of M-payment in formulating users’ perceived benefits and design system attributes accordingly under the pandemic situation. Meanwhile, M-payment service providers should guarantee the compatibility, efficiency and security of transactions to meet customers’ requirements and match their lifestyles. In addition, enhancing the public impression of M-payment and stimulating a positive word-of-mouth social effect would improve the technology providers’ reputation in different situations.
Third, this study supports new technology providers with a comprehensive understanding of customers’ adoption intentions, determined by technological and psychological perceptions conjointly. Consequently, relevant stakeholders should focus on taking advantage of the features of technology (such as the contactless characteristic of M-payment) corresponding to its benefit to a particular situation (such as avoiding direct or indirect contacts to decrease COVID-19 transmission risk) in terms of maintaining service quality, reliability and efficiency to meet consumers’ physical and mental concerns and optimize their experience, thereby increasing acceptance among the target population.
Finally, the findings and results of this study could be applied as references for other online-to-offline (O2O) service industries in a pandemic situation. Relevant businesses could utilize the results to develop appropriate strategies that combine the benefits of technology characteristics with users’ technological perceptions and mental expectations to expand markets to adapt to different emergency situations and build better customer bases.
8. Limitations and Future Research
There are several limitations inherent in this study which need to be acknowledged. Firstly, the data collection was restricted to China during a particular period of the COVID-19 pandemic; the results may not be generalized to different countries and various situations. Future studies should replicate this model and collect data from different nationalities and consider specific benefits corresponding to particular situations. Furthermore, the research model can be examined through cross-cultural studies for better understanding of the variations in different cultural backgrounds.
Secondly, there were limited variables and interactions of the variables analyzed in this study—e.g., the variables selected in this study were mainly from a technology adoption aspect. Future research can put more effort into integrating the relations between variables, such as social influence affecting perceived security [15
] and use technological indicators with the variables from a health and risk aspect. Meanwhile, in order to gain a deeper understanding of the mental and technological factors affecting adoption intentions with regard to novel technology, future research can incorporate research models with other variables, such as a cultural moderator, satisfaction, etc., which are also recommended in previous studies [42
Thirdly, as the data collection period was limited, and that data were homogeneously distributed and collected through Wechat (a mobile social media application in China), in this study, the data collection process is recommended to chronically and integrally cover the users from different areas (urban and rural areas) over a different period of using M-payment in various patterns (online and offline surveys).
Finally, there was no distinction between the types of M-payment patterns (such as SMS, NFC and QR), platforms of M-payment (such as Apple pay, Samsung pay, Wechat pay, and Alipay) and patterns of electronic transaction (such as, electronic transaction via computer, electronic transaction via mobile device). Therefore, a future study can focus on distinguishing the different payment methods or payment platforms of M-payment techniques in accordance with specific research objectives.
In conclusion, we proposed a theoretical adoption model integrating UTAUT with perceived benefits from MAT and two additional variables, trust and perceived security, to appropriately explain the mental and technological factors affecting users’ behavioral intentions of adopting M-payment during the COVID-19 pandemic in China. This research model provided extensive explanatory power when explaining that users’ payment habits had changed due to the influence of pandemic, and that adoption intentions of M-payment were determined by technology perceptions and mental expectations conjointly. Performance expectancy, perceived benefits, social influence, trust and perceived security are significant in facilitating users’ adoption intentions of M-payments during the COVID-19 pandemic. Specifically, the contactless characteristic of the M-payment technique is beneficial in maintaining social distancing and protecting personal safety under a pandemic. This study also explored new causal relationships and found that perceived benefits are significantly determined by social influence and trust. Moreover, performance expectancy is influenced by effort expectancy and trust, towards explaining users’ behavioral intentions of using M-payments during the COVID-19 pandemic.
Furthermore, this study provides several significant theoretical and practical contributions on on investigating novel technology adoption in a particular situation, which contributes to the knowledge and understanding of the extension of the UTAUT application, explaining that users’ payment habits have changed because of the pandemic and adoption intention of M-payment is determined by users’ technological perceptions and mental expectations. In addition, this study recommends that researchers and relevant stakeholders focus on a particular characteristic of M-payments that corresponds with the pandemic, which can influence the perceived mental and technological benefits of the user. Understanding users’ behaviors is an efficient way to analyze new technology adoption and develop an appropriate strategy for optimizing users’ experiences.