1. Introduction
Cashless transactions are on the verge of becoming the norm, with the potential to render physical transactions with fiat currency obsolete. The market’s strategic industries, such as tourism [1], business [2], and healthcare [3], have adopted the new innovation of digital payments. According to reports, the advent of FinTech [4,5] and the COVID-19 pandemic [6] have contributed to the emergence of cashless transactions. E-wallets are developing as one of the mechanisms for severing the transmission chain, as they foster social distance [7,8]. Virus-related health risk avoidance is claimed to have a major impact on the users of e-wallets [9] and other related technological applications [10,11]. COVID-19 further contributes to the growth of cashless transactions by driving national digitalization [12], causing a massive surge in cashless transactions [13], making consumers more receptive to electronic banking [14], and ensuring adherence with health-related regulations linked with the virus [15]. After the pandemic, the digital transaction ecology has become more mature and competitive [16]. Studies in the United States, Great Britain, Japan, Canada, and Australia throughout the outbreak divulged a marked increase in the digitalization of transactions [17]. In the future, cashless transactions will be the primary method of payment.
E-wallet is one of the key vehicles for propagating the innovation offered by FinTech as it is secure, mobile, and easily accessible. E-wallet is defined as a method of digitalized payment in which the available funds are held on a server as opposed to a chip [18]. It was also described as an electronic card that enables digital transactions via smartphone [19]. This FinTech invention’s most significant contribution is the virtualization of debit and credit cards, which eliminates the need for consumers to carry this physical financial medium and offers a new level of innovation in monetary transactions [20]. In the past few years, e-wallets have evolved into a method for tracking transactions with a focus on cost effectiveness [21]. Additionally, corporate organizations are recommended to develop business strategies to capture the intent of prospective clients in the e-wallet market share [22]. Thus, e-wallets merit study in light of modern structural adjustment toward the digital economy, as digitalization shapes the global economic landscape [23,24]. This is backed by the fact that e-wallets are also gaining popularity in the G20 member states, Indonesia [25,26] and India [27].
Despite the increasing popularity of e-wallets, obstacles and barriers continue not only in industrialized nations but also in emerging ones such as Malaysia [28]. Simultaneously, Gen Z is the driving force behind FinTech innovations such as e-wallets [29]. According to the study, the introduction of e-wallets facilitates the broad financial inclusion of society [30] and may sometimes result in excessive spending by youngsters [31]. However, security concerns such as fraud impede the adoption of such technology [32]. At the same time, there is a need for further research on the topic of technology for specific age groups [33,34,35].
Numerous studies on e-wallets with diverse perspectives and outcomes have been published worldwide. To ensure the continued use of e-wallets, it is advocated that trust should be prioritized as the significant driver [36]. The different phenomena are noticed as Perceived Usefulness (PU), and it is also noted that trust does not affect the continued use of e-wallets [37]. During COVID-19, the intention to continue using an e-wallet is mediated by subjective wellbeing and impacted by perceived security [38]. The majority of these studies were undertaken to comprehend the acceptance [39,40,41] and continued use of e-wallets [42,43]. Nonetheless, a significant proportion of research has focused on the adoption of e-wallets rather than their continued use [44,45,46]. The acceptance of e-wallets was researched using the Technology Acceptance Model (TAM) [47,48,49] and the Unified Theory of Acceptance of Technology Usage (UTAUT) [22,44,50]. In the case of Gen Z customers, however, a significant number of research gaps exist in terms of e-wallet acceptance and TAM, such as a lack of focus on Gen Z [51] and the motivation element receiving less consideration when attempting to comprehend e-wallet acceptance.
Individual self-efficacy is believed to influence an individual’s acceptance of a technology introduced in the financial sector in general [52]. In other technology, the analogous posture can be evidenced [53,54,55]. Self-efficacy has been empirically demonstrated to influence Gen Z’s acceptance of technology. For example, a study on contactless services [56], healthcare wearables [57], and cyberloafing [58] indicates that self-efficacy influences Gen Z. However, it is unsure how self-efficacy directly impacts the adoption of e-wallets, given few studies on e-wallets highlight self-efficacy as a factor leading to their acceptability [20,45,48]. This creates a vacuum in our modern understanding of self-efficacy.
Thus, the present study is going to fill in the research gaps by extending TAM with the motivation element to better understand the factors that influence the acceptance of e-wallets among Gen Z—as motivation has been long neglected in understanding the acceptance of e-wallets. Furthermore, this study will bridge the knowledge gap about the role of self-efficacy in the acceptance of e-wallets among Gen Z. Eventually, our research would allow for a much deeper and broader knowledge of the acceptance of e-wallets from the orientations of TAM, motivation, and self-efficacy among Gen Z, who are currently the foremost trendsetters of new technologies in the information age. Even though FinTech is rapidly advancing with many forms of technologies, constraints such as slow adoption among users linger [59], necessitating extensive research on the acceptance of FinTech innovation among users, such as the study we are undertaking.
This study would substantially contribute to the body of knowledge by providing a starting point for future research on e-wallets, particularly in relation to certain age brackets. It contributes to the literature on TAM as one of the most researched models for comprehending technology acceptance. This research will contribute to the expansion of motivation theory into e-wallet acceptance and TAM in order to enlighten the industry and scholars about the current situation of the most recent FinTech innovation, in this instance, e-wallets among consumers.
Due to the fact that Gen Z is regarded to be distinct from past generations, which may affect the global market [60], now is the ideal time for academics and businesses to do studies comparable to the one we have conducted. Gen Z is also suggested as highly influential toward FinTech technologies [29]. Simultaneously, the globe is going toward cashless transactions, particularly after the outbreak of the pandemic [61]; both cashless transactions and the pandemic bring about enormous changes. As the pandemic revealed a considerable increase in affective elements such as stress levels among individuals [62,63], it is believed that affective factors such as motivation in technology will become increasingly important in the future [64]. Consequently, based on the notion of timeliness, our current research is timely.
In order to fulfil the purpose of our research, the paper is structured into seven sections, including this Introduction section. In the Literature Review and Hypotheses section, hypotheses are formulated based on the identified research gaps in the literature review. This enables the researchers to ensure not only the soundness of the ideas but also the novelty of the research by filling identified gaps in the current literature. The third section of the methodology links the hypotheses to their methodological implementation and sampling technique. This leads to a wide range of data analysis in Section 4, which assesses the acceptance or rejection of the hypotheses using a series of detailed statistical analyses utilizing the Covariance-based Structural Equation Model. In Section 5, the significance of the study and its theoretical and practical inputs to the current body of knowledge are addressed. In Section 6, we address the limitations of our study and how it may contribute to future research, and in Section 7, we draw conclusions based on empirical studies.
2. Literature Review and Hypotheses
To address the aforementioned research gap, TAM, Self-Determination Theory (SDT), and self-efficacy theory were combined to probe Gen Z’s acceptance of e-wallets. TAM is incorporated as the predominant framework for understanding technology acceptance since it is a widely acknowledged paradigm [65,66,67]. SDT by Deci, Connell, and Ryan [68] is incorporated as a motivational theory to comprehend the acceptance of e-wallets. For Bandura’s [69] theory of self-efficacy, the theory serves as a means to comprehend the role of self-efficacy in the acceptance of e-wallets, which has been neglected in the current state of study. The literature review begins with SDT, then self-efficacy, and concludes with TAM to accommodate the articulation of hypotheses.
2.1. Self-Determination Theory (SDT)
SDT was introduced in 1989 [68]. It is the most predominant theory of motivation [64]. This theory comprises of six mini-theories: Cognitive Evaluation Theory, Organismic Integration Theory, Basic Psychological Need Theory, Causality Orientations Theory, Goal Content Theory, and Relationship Motivation Theory. The theory’s practical value in different domains has been thoroughly validated [70]. Yet, several research gaps exist regarding SDT.
First, to the best of our knowledge, SDT was never incorporated into the comprehension of the acceptance of e-wallets as motivation, which receives inadequate consideration. Precisely, SDT was never studied to comprehend Gen Z’s acceptance of e-wallets. In contrast to other theories, such as the Theory of Planned Behavior [7,71], Task-Technology Fit [47], which is more cognitive in nature, has gained substantial attention for explaining the acceptance of e-wallets.
The second research gap is that SDT has not yet been adequately integrated with other theories, which may restrict our knowledge of the theory’s full capacity to comprehend human motivation [64]. It is suggested that the future of SDT will involve the combination of SDT with technology understanding [72]. Consequently, this study attempts to fill this deficiency by integrating SDT and TAM to fully grasp Gen Z’s acceptance of e-wallets.
The third knowledge gap is that SDT was primarily researched from the standpoint of basic psychological needs, which include autonomy, competence, and relatedness [73,74]. Whereas, other SDT variables, such as Intrinsic Motivation (IM), Identified Regulation (IR), External Regulation (ER), and Amotivation (A), remain largely unexplored and represent a substantial gap in the advancement of SDT as a motivational theory [64]. Ultimately, this raises the question of what role IM, IR, ER, and A contribute to anticipating the acceptance of e-wallets among Gen Z based on the implicit nature of TAM.
To address the stated research gaps, this study will incorporate SDT into TAM so that the function of SDT based on IM, IR, ER, and A may be comprehended. We anticipated that SDT in the form of IM, IR, ER, and A as a motivating theory could serve as a predictor for the two TAM variables which PU based on findings from prior research on various technical advancements [75,76]. The acceptance of e-wallets has not been studied with a focus on motivation [77,78], hence its actual influence remains largely unknown. In lieu of specifying the magnitude or direction of the predictive role played by IM, IR, ER, and A, we opt to assume that these SDT factors would impact PU and PEU positively, except for A which is proven to function as a negative determinant [79,80]. Thus, the following hypotheses were formulated to understand the relationship:
IM positively and significantly impacts Gen Z’s PU about e-wallets.
IR positively and significantly impacts Gen Z’ PU about e-wallets.
ER positively and significantly impacts Gen Z’s PU about e-wallets.
A negatively and significantly impacts Gen Z’s PU about e-wallets.
The second factor that exists in TAM as the connection toward external factors is Perceived Ease of Use (PEU). PEU is the perception that a technology is easy to operate, and its benefits outweigh the effort necessary to operate the technology [81]. In addition to PU, the effects of IM, IR, ER, and A on PEU are relatively unexplored at this time. Some research investigated e-wallets without PU and PEU predictors [22,82,83] or lacking any motivation theory as predictors [47]. As the nature of the relationships was uncertain, we opt to infer that IM, IR, and ER will have a positive impact on PEU, and A will have a negative impact on PEU. This led to the formulation of these hypotheses:
IM positively and significantly impacts Gen Z’s PEU about e-wallets.
IR positively and significantly impacts Gen Z’s PEU about e-wallets.
ER positively and significantly impacts Gen Z’s PEU about e-wallets.
A negatively and significantly impacts Gen Z’s PEU about e-wallets.
2.2. Self-Efficacy
Bandura popularized the self-efficacy notion [69]. Self-efficacy has been demonstrated to increase performance [84] and impact individual motivation and selection process [85]. Despite the fact that the theory has been established for decades, critical research gaps remain undiscovered.
Most of the research about self-efficacy was conducted in the western part of the globe. Despite efforts to undertake self-efficacy research in Africa [45] and a mixture of Africa and Europe [86], the sample group of Gen Z continues to be overlooked. This research will contribute considerably to the advancement of self-efficacy research and the resolution of self-efficacy-related cultural gaps by presenting fresh information regarding self-efficacy in the context of Asia and utilizing samples from Gen Z.
The second shortcoming is that self-efficacy is one of the most prevalent factors incorporated in TAM when attempting to comprehend human acceptance of technology [87,88,89]. This is not the case, however, when it comes to studying the acceptance of e-wallets [32,49]. Consequently, the influence asserted by self-efficacy on the acceptance of e-wallets is unclear, particularly with regards to Gen Z. To investigate the influence of self-efficacy, we present Self-Efficacy (SE) as an external variable of TAM, based on study conducted by Compeau and Higgins [90]. The following hypotheses were therefore proposed:
SE positively and significantly impacts Gen Z’s PU about e-wallets.
SE positively and significantly impacts Gen Z’s PEU about e-wallets.
SE by Compeau and Higgins [90] has developed to incorporate Digital Media Self-Efficacy (DMSE) as a variation [91]. As digital devices such as tablets and smartphones become increasingly indispensable to human existence, the necessity to comprehend DMSE is becoming more vital than ever [91]. In the 21st century, the effect of DMSE on youngsters, such as university students (who are primarily members of Gen Z), is enormous, particularly following the digitization wave brought about by the pandemic [92]. In the near future, DMSE will play a pivotal function in the digital realm, as it may have an impact on the most recent evolution in education, such as home-based learning [93]. Unfortunately, DMSE is still unexplored when attempting to comprehend the acceptance of e-wallets among all sample types, including Gen Z. Thus, the following hypotheses were proposed:
DMSE positively and significantly impacts Gen Z’s PU about e-wallets.
DMSE positively and significantly impacts Gen Z’s PEU about e-wallets.
2.3. Technology Acceptance Model (TAM)
TAM is a widely employed model of technology acceptance comprising four factors which are PU, PEU, Attitude Toward Technology (AT), and Behavioral Intention (BI) [94]. The model consisted of three primary responses: cognitive response (predicted by PU and PEU), affective response (based on AT), and behavioral response (predicted by BI) [94]. The acceptance of a technology is associated with BI, and BI is determined by PU and PEU as the main determinants via AT as a mediator. The model has been implemented in numerous FinTech-related fields, including understanding the acceptance of cloud-based enterprise [95], financial portals [96], digital Islamic banks [97], and digital investment services [98]. Nonetheless, this model also garnered numerous criticisms that make room for further improvement and exploration.
One of the shortcomings of TAM is that it concentrates cognitive elements such as PU and PEU but not motivation, especially IM [87]. This research, which combines TAM with SDT and incorporates IM, contributes to the expansion of TAM scholarship. Combining TAM with external factors such as SDT and SE increases its explanatory power, allowing for a more in-depth explanation of the technological acceptance phenomenon [99]. There is also evidence that the pandemic drastically impacted the financial behavior of the worldwide population, as indicated by the increased frequency of digital transactions [100]. Yet, understanding the acceptance of e-wallets in depth remains an important central purpose [101,102]. The acceptance of e-wallets by Gen Z based on TAM is still a solid research gap. Thus, to further comprehend Gen Z’s acceptance of e-wallets, the following hypotheses will be tested:
PU positively and significantly impacts Gen Z’s AT about e-wallets.
PEU positively and significantly impacts Gen Z’s AT about e-wallets.
PEU positively and significantly impacts Gen Z’s PU about e-wallets.
AT positively and significantly impacts Gen Z’s BI about e-wallets.
Most studies opt to remove AT from TAM due to the notion that it has a weak mediating effect. However, because e-wallets are a relatively new technology, their influence may differ. This research will retain AT as a factor of TAM and evaluate its mediating function. On the premise of the formulation of these 16 hypotheses, the testing of the following theoretical framework is proposed as in Figure 1.
2.4. Justification for Integrating SDT, Self-Efficacy, and TAM into Extended TAM
In order to integrate TAM with SDT, the SDT variables IM, IR, ER, and A were positioned as prospective determinants of PU and PEU. Two variables representing Self-Efficacy, SE and DMSE, were connected to TAM as predictive factors of PU and PEU using the same approach. The practice of associating external variables as predictors of PU and PEU is widespread in TAM research [103,104]. It is also utilized in Venkatesh’s popular literature about TAM [105]. Other strategies of incorporating external variables into extended TAM, such as direct linkage toward adoption [106] or AT [107], are also employed. Davis, the model’s founder, proposed extending TAM by connecting external factors to PU and PEU [94]. Our current strategy of incorporating external factors into the extended TAM via PU and PEU is therefore justified.
The integration of SDT, Self-Efficacy, and TAM is intended to close research gaps regarding e-wallets, as earlier studies paid insufficient attention to users’ motivation and efficacy regarding the technology. The extension of TAM also overcomes the shortcomings of the original TAM by enhancing the model’s explanatory power, as TAM has been criticized for being overly focused on cognitive processing and lacking an emotive paradigm [99]. The inclusion of SDT resulted in an extended TAM, which made it possible to test a superior model spanning two aspects of technological usage, namely the cognitive and attitude-based model of TAM and the motivational model based on SDT. The incorporation of SDT is also supported by Ryan and Deci’s argument, which advocates combining SDT with other relevant frameworks given that the theory was designed to allow researchers to extend it [70]. Self-efficacy integration into TAM enables us to comprehend the crucial function of technological efficacy in determining user response to technology [108], in this case, e-wallets. This strategy is also consistent with the proposal provided by prior research based on a comprehensive systematic literature review that self-efficacy is one of the required external variables for TAM [87,89]. On the basis of the stated justification, we propose extending TAM by incorporating SDT, self-efficacy (using SE and DMSE as variables), and TAM.
3. Methodology
This study employs a correlational research design to comprehend the relationships between the variables under investigation, and then proposes a framework of e-wallet acceptance based on the tacit of expanded TAM, SDT, and self-efficacy. The following describes the methodology:
3.1. Samples
The questionnaire was distributed to young Malaysians via Google Form for online distribution. The samples were collected using the technique of convenience sampling. The convenience sample technique was used to ensure that each sample in this study has access to an e-wallet and has prior familiarity with its use. In addition to having access to and experience with e-wallets, the primary characteristic of the sample is that they are affiliates of Gen Z. This generation consists of individuals born in 1995 or later who already are currently 28 years old or younger [109]. Thus, convenience sampling was initiated to ensure that all of these criteria are met prior to sample participation in the study. We do not employ random sampling, such as randomly selecting members of Gen Z, as this would be impractical given that not all members of Gen Z are familiar with FinTech innovations such as e-wallets [110]. All of the samples provided consent at the beginning of the questionnaire before commencing to answer the questions. In total, 233 individuals responded to the questionnaire, and all questions were answered as required by the Google Form settings, resulting in the absence of any missing data. The sample size determination is likewise based on a 95 percent confidence interval with a margin of error of 6.5 percent, resulting in a minimum sample size of 228 that is exceeded by this study. The sample size of 233 fulfills the required sample size based on Hair’s 20:1 ratio, which requires 20 observations per independent variable [111]. This study has nine independent variables, indicating that a minimum sample size of 180 is necessary. This sample size range is further supported by Kline’s suggestions that sample sizes well over 200 should be regarded as large and sufficient for Structural Equation Model analysis [112]. According to Roscoe’s guideline, the sample size of 233 for this study meets the suggested sample size range of 30 to 500 samples for minimizing Type II error [113]. Thus, the sample size of 233 exceeds the minimum required sample size and is consistent with the recommendation and guideline for determining sample size [114].
The samples included 123 males (52.8% of the total) and 110 females (47.25%). Regarding age, the age distributions were as shown in Table 1.
Overall, all the samples have experience interacting with e-wallet technology in their daily life and have access to e-wallets in term of smart devices, banking facility, as well as e-wallet payment mode which is set as the prerequisite to join this study as sample.
3.2. Instrument
This study employs a questionnaire that was developed based on the work of pioneering researchers in the field. The questionnaire contains 48 questions organized into 4 sections. The Section 1 gathered demographic information about the samples, including their age and gender. Based on SDT, Section 2 consists of tasks that explore the IM, IR, ER, and A. Section 3 asks samples about their SE and DMSE, while the Section 4 measures the acceptance of e-wallets based on TAM. All TAM, SDT, SE, and DMSE questions use a 5-point Likert scale. There are a total of 12 items measuring SDT theory, with 3 items measuring each variable. Self-efficacy is represented by 2 variables that are measured by 7 items for SE and 5 items for DMSE, for a total of 12 items that measure the self-efficacy theory. For TAM, 24 items were used to measure this model, with 6 items measuring PU and 6 more testing PEU. Two more variables of TAM, BI and AT, are represented by eight and four items, respectively. The items are as in Appendix A.
3.3. Data Analysis
The data were analyzed using inferential statistics. The inferential statistics involved high-end analysis using Covariance-based Structural Equation Model via IBM SPSS AMOS 28 and IBM SPSS Statistics 27. The Structural Equation Model was executed in four phases involving Confirmatory Factor Analysis (CFA), Measurement Model, Structural Model, and, finally, the inspection of mediation analysis.
The CFA will examine the loading of items into each construct, Construct Reliability (CR), and Average Variance Extracted (AVE), which comprise the Convergence Validity of the instrument. The CR was computed using the following formula [115,116]:
The purpose of CR is to evaluate the reliability of the tested variable. The calculation of CR requires two fundamental values: the factor loading, λ, and the measurement error, . The following formula was utilized to calculate AVE [116,117]:
The AVE is calculated in order to ensure the Convergent Validity of the study variable, where n is the number of indicators or items. Simultaneously, the model fit of the CFA was evaluated based on the respective theories of TAM, SDT, and self-efficacy. Cronbach’s alpha reliability is also reported to further validate and corroborate the Convergence Validity produced by CFA analysis [116].
The Measurement Model will examine the link between the observable and latent variables in this study. This test will guarantee that there is no strong correlation or association between the variables to ensure that the Structural Equation Modeling analysis produces valid and reliable results. In this stage, the Discriminant Validity of the data is also evaluated using the Heterotrait–Monotrait ratio of correlations (HTMT), which has been shown to be suitable for the Covariance-based Structural Equation Model [99]. Additionally, Discriminant Validity will aid the researcher in identifying any multicollinearity issues with the data.
The Structural Model permits researchers to examine the proposed relationships between variables in the theoretical model and to comprehend the nature of each interaction involved. The Structural Model serves as the foundational test that determines whether or not this study’s presented hypothesis is accepted or rejected. Finally, the Structural Model will be used to investigate the variables’ mediation analyses. The comparative fit index (CFI), Tucker-Lewis index (TLI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA) are used to evaluate the models using benchmark values established by Hu and Bentler [118].
4. Findings
4.1. Confirmatory Factor Analysis (CFA)
Based on theories integrated into the theoretical model, the CFA was performed. All 16 items from the SDT theory, 15 things from the self-efficacy theory, and 26 items from the TAM theory were evaluated. Table 2 displays the outcome of the convergence validity of the items and variables.
The CFA analysis proves that each and every theory or model has a good fit based on fit indices threshold recommended by Hu and Bentler [118]. With χ2 = 171.368, df = 48.000, χ2/df = 3.570, CFI = 0.929, TLI = 0.903, and SRMR = 0.062, the fit indices for SDT are more than satisfactory. The same holds true for self-efficacy, which has χ2 = 172.996, df = 53,000, χ2/df = 3.264, CFI = 0.938, TLI = 0.922, and SRMR = 0.006. χ2 = 708.577, df = 246.000, χ2/df = 2.880, CFI = 0.937, TLI = 0.930, and SRMR = 0.015 are all excellent fit indices for TAM’s CFA. Nonetheless, a number of items were eliminated as a result of their poor loading into the variable and significant influence on other items in the same variable. These are designated as IM2, IR4, ER3, A4, SE2, SE9, DMSE4, and AT4. When measuring fit indices, CR, and AVE, these items were omitted in their whole. The remaining items are loaded in excess of 0.50 in accordance with Hair’s item load recommendation [111]. The fact that the minimum loading is 0.71 and the maximum loading is 0.93 indicates that the items have been placed in the correct variable. The CR and AVE outputs of the CFA tests indicate that all the items and variables meet the specified criteria of CR = 0.70 [111] and AVE = 0.50 [115] for all items and variables, respectively. This indicates that both the data and the instrument have a high level of Convergent Validity and are eligible for future testing.
4.2. Measurement Model
Based on the 10 variables proposed by the theoretical model, the Measurement Model was constructed. Again, the fit indices proposed by Hu and Bentler [118] were utilized as the cutoff value to establish the model’s quality. The Measurement Model fit measures are good with χ2 = 2145.029, df = 989.000, χ2/df = 2.169, CFI = 0.902, TLI = 0.893, and SRMR = 0.051. The analysis continues with an examination of the data and Discriminant Validity of the instrument using HTMT. This study’s Discriminant Validity is summarized in Table 3.
As reflected by Table 3, all variables demonstrate Discriminant Validity with the exception of the test between IM–IR and IR–ER, which demonstrates an HTMT value greater than 1.0. This indicated that there is no Discriminant Validity between these variables, as there is a strong correlation between IM–IR and IR–ER. The samples regarded IM and ER to be identical to IR, and multicollinearity may exist amongst the variables, jeopardizing the study’s conclusion. To address this issue, IR was eliminated from future testing and Discriminant Validity was determined.
The new Measurement Model have a better model fit measures compared to the first Measurement Model with χ2 = 1842.548, df = 866.000, χ2/df = 2.128, CFI = 0.912, TLI = 0.904, and SRMR = 0.047.
4.3. Structural Model
The Structural Model was developed using the theoretical model devoid of IR (as IR was omitted due to Discriminant Validity issue and possible multicollinearity). The Structural Model has excellent fit index values (χ2 = 1894.191, df = 878.000, χ2/df = 2.157, CFI = 0.908, TLI = 0.901, SRMR = 0.049, and RMSEA = 0.071). The Structural Model is as in Figure 2.
The hypotheses were tested by examining the estimates of the Structural Model’s generated pathways. The hypotheses testing is as seen in Table 4.
The association between IM and PU was negligible (β = −0.194, SE = 0.163, p > 0.05), as was the relationship between IM and PEU (β = −0.139, SE = 0.176, p > 0.05). ER to PU was significantly positive (β = 0.520, SE = 0.216, p = 0.005); ER has a favorable effect on PEU (β = 0.430, SE = 0.219, p < 0.05). A is insignificant toward PU (β = −0.028, SE = 0.036, p > 0.05) and PEU (β = −0.034, SE = 0.041, p > 0.05). SE has a positive correlation with both PU and PEU (β = 0.270, SE = 0.079, p < 0.001: β = 0.528, SE = 0.081, p < 0.001). DMSE is insignificant to PU (β = 0.095, SE = 0.062, p > 0.05) but positively correlates to PEU (β = 0.151, SE = 0.071, p = 0.010). All TAM variable relationships are positive, as predicted by the core model [81]. Positive correlations between PU and PEU toward AT are very substantial. PU exert a bigger impact on AT with β = 0.517, SE = 0.076, p < 0.001 compared to PEU, which has a positive correlation with a somewhat smaller magnitude; there is a weaker positive association (β = 0.452, SE = 0.072, p < 0.001). PEU contributes positively to PU, and the association is substantial (β = 0.307, SE = 0.085, p < 0.001). Finally, it was determined that the AT is a key indication of BI with a very profound effect at β = 0.941, SE = 0.085, p < 0.001. With a variance for BI of 89 percent (R2 = 0.89), AT of 88 percent (R2 = 0.88), PU of 87 percent (R2 = 0.87), and PEU of 80 percent (R2 = 0.80), the model as provided in the theoretical model and tested in the Structural Model has an excellent capacity for explanation.
4.4. Mediation Analysis
The purpose of the mediation analysis was to comprehend the mediation role played by AT in the Structural Model. Our investigation of mediation employs the Bootstrap Method with 5000 bootstrap samples and a 95 percent biased-corrected confidence interval. First, the mediation model, indirect model, and direct model were evaluated for their respective fit indices as in Table 5.
To gain a deeper understanding of the potential mediation impact of AT, the total effect, indirect effect, and direct effect of the link between PU and PEU on BI via AT were analyzed, as shown in Table 6.
The total effect between PU–AT–BI is significant with β = 0.465, p < 0.05, LB = 0.213, and UB = 0.751 according to the bootstrap analysis of mediation. The total effect of PEU–AT–BI is also significant (β = 0.444, p < 0.05, LB = 0.189, and UB = 0.681). This suggests that PU, PEU, and AT have an effect on BI, implying that mediation is conceivable. Consequently, the test also examines the indirect effect of the linkages. For the indirect effect, AT gives impact in relationships between PU and BI (β = 0.463, p < 0.05, LB = 0.216, and UB = 0.711) as well as PEU and BI (β = 0.429, p < 0.05, LB = 0.183, and UB = 0.707). In summary of the mediation analysis, AT is the full mediator of the interactions between PU and PEU and BI.
5. Discussion
The aim of this research is to comprehend the acceptance of e-wallets among Gen Z based on the implicit theories of TAM, SDT, and self-efficacy. When attempting to comprehend the acceptance of FinTech applications such as e-wallets, motivation has been overshadowed for a very long time. Self-efficacy is also absent from the highlight, despite the fact that both motivation and self-efficacy have been reported to influence human acceptance of other types of technology [76,119]. This research investigates these research gaps. SDT, comprising IM, IR, ID, and A, was integrated into TAM via PU and PEU in order to comprehend the effect of motivation. Two constructs, SE by Bandura and DMSE by Hammer, Scheiter, and Sturmer [91], were incorporated into TAM as predictors of both PU and PEU to comprehend self-efficacy. This work not only fills in research gaps regarding Gen Z’s acceptance of e-wallets but also knowledge gaps regarding TAM [87], SDT [64], and self-efficacy [85].
To fully comprehend Gen Z’s acceptance of e-wallets, the mediation effect of AT is also being explored. We performed a mediation analysis of the influence of AT on the relationship between PU and PEU and BI. All of the statistical analyses of CFA, Measurement Model, Structural Model, and bootstrap mediation analysis led to the testing of all hypotheses and the mediation effect. Out of 16 hypotheses being tested, 9 of them are supported. The conclusion of the study is depicted in the Model of E-Wallet Acceptance among Gen Z as seen in Figure 3.
The result indicates that the model can explain 89% of the variance in Gen Z’s acceptance of e-wallets. This indicates that just 11 percent remain unexplained by the model, which surpasses the basic TAM model [81] and subsequent TAM-based research [120,121]. All the main relationships in TAM are supported by hypotheses 13 (PU to AT), 14 (PEU to AT), and 15 (AT to BI). AT serves as a mediator between PU and PEU and BI, which acts as a full mediator. This contradicts the previous finding [120]. AT was suggested to be omitted from TAM due to its limited involvement in mediating the links between PU and PEU to BI [122]. The limited mediation of AT may be attributable to the familiarity of the test subjects with the technology being evaluated. As emerging technologies such as AI [123], robots [124], and VR [125] suggest, AT will reappear as a mediator for BI. E-wallet is a relatively new technology in FinTech which would validate our conclusion that AT is pivoted as a full mediator. PU remains the most significant predictor of AT (β = 0.517) compared to PEU (β = 0.452), which is in-line with other research [126]. This implies that e-wallet providers and FinTech-related stakeholders should ensure that Gen Z perceives e-wallets as useful and necessary in order to achieve a high level of acceptance among them. For Gen Z to accept this innovation, e-wallets must be user-friendly and simple to operate. The ease of use of e-wallets also contributes to the perception of e-wallets’ usefulness but only to a small degree, β = 0.307, contradicting findings about the effect of PEU on PU in higher education but interestingly involving Gen Z in the context of IoT and smart classroom [127]. Research in digital comics [128] and digital mental healthcare [129] supports our finding. We infer this is because the Gen Z PEU will only influence their PU’s viewpoint toward a technology if they appear to have a powerful incentive to use it, such as the hedonic feeling of digital comics, the need to remain healthy for digital mental healthcare, the urgency to use virtual learning environment [130], and the desire to complete digital transactions for our e-wallets context.
The research indicates that only the relationship between ER and PU and PEU is significant from the standpoint of SDT. This implies that Gen Z utilizes e-wallets due to external circumstances rather than their own self-motivation, as indicated by IM. Where IM represents activities undertaken because of intrinsic enjoyment [72,131], the absence of IM function as a predictor of acceptance may suggest that e-wallet usage is driven by external factors. This inference is supported by fact that ER which is part of external motivation positively influences PU (β = 0.520) and PEU (β = 0.430). This conclusion somewhat corroborates earlier research regarding the acceptance of technology-enhanced learning among Gen Z-dominated university students [99]. This observed that Gen Z’s embrace of e-wallets is mostly impacted by their desire for externally controlled rewards [131]. It also suggests that the acceptance of e-wallets is less self-directed and more toward controlled motivation, resulting in low levels of motivation. Consistent with Chen and Zhao’s [75] assertion that university students who are physically members of Gen Z regarded their controllable motivation to be significant in terms of how they viewed the usefulness and usability of a mobile application. We believe that the widespread use of e-wallets at retail locations, the inconvenient nature of utilizing fiat currency, and the efficacy of digital transactions as compared to non-digital transactions are the primary drivers of e-wallets’ acceptance. We suggest that, for e-wallet providers and policy makers, to increase the acceptance of e-wallets, particularly among Gen Z, the availability of e-wallets, such as all outlets or business premises providing e-wallet facilities, and other contributing factors such as good Internet access and an excellent mobile application interface of the e-wallet would aid in the acceptance of this new FinTech technology.
Self-efficacy is a cognitive locus of operation that influences whether or not an individual’s coping activity is initiated and influenced by an emotional source [69]. In this work, self-efficacy is offered as an external variable that influences the PU and PEU of samples. In accordance with the literature [127,132], the sophisticated statistical analysis undertaken revealed that all self-efficacy-related hypotheses are valid. The substantial SE-PEU link was also noted in the literature [133]. Prior studies indicate that measures of self-efficacy, such as computer self-efficacy, contribute to higher BI in adolescents’ technology acceptance [134]. Our research indicates that the correlations between SE and PU and PEU are favorably significant (β = 0.270, p < 0.001; β = 0.528, p < 0.001), lending support to this position. The conclusion supports Bandura’s [69] primary premise that human conduct is influenced by their expectation of efficacy. Therefore, for e-wallets to be widely accepted, potential users must have a high level of self-efficacy in performing the associated digital transaction task. This paradigm of efficacy can be enhanced by providing a mobile application for e-wallets with an intuitive user interface and a solid infrastructure to support the essential technology.
Lack of research on DMSE, a new form of self-efficacy, is one of the research gaps in self-efficacy. Only a few studies on DMSE have been undertaken [91,92,93]. As a result of a lack of attention, the role and amplitude of DMSE in relation to technology acceptance in the context of e-wallets and beyond, as well as engaging Gen Z as the sample or a broader age range, are unknown. This is the fourth study on DMSE that significantly contributes to the growing body of literature on this knowledge gap. In this study, DMSE was classified in the same category as SE as an external factor of TAM by predicting the PU and PEU of samples. DMSE influences the acceptance of e-wallets among samples by predicting their perception of the wallet’s functionality, however, at a weak magnitude, β = 0.151, p < 0.05. In essence, SE is more influential than DMSE in predicting acceptance, indicating how efficient Generation Z is at using the e-wallet in terms of their ability to adapt to the digital transaction ecosphere, infrastructure, and technological support is more significant than their acquaintance with gadgets such as the smart phone and tablet.
5.1. Theoretical Contributions
This research makes numerous substantial contributions to the theoretical viewpoint and the growth of knowledge. First, this research study focuses on the acceptance of e-wallets from the tacit of TAM among Gen Z, a subject that has not yet been adequately studied or understood. To date, little is documented about the factors that impact Gen Z’s acceptance of e-wallets. It contributes further to the advancement of this paradigm as a robust theory applicable to technologically oriented social science research. Our findings imply a better theoretical grasp of e-wallet acceptance among Gen Z, but significant growth outside this age group is also conceivable. By including SDT, SE, and DMSE into TAM, it is possible to increase theoretical underpinnings of how motivational factors, which were originally overlooked in TAM due to cognitive nature, might improve human acceptance of technology, in this case, e-wallets. This study is also an attempt to integrate SDT with other appropriate theories, and it demonstrates the viability of such an integration. This research also contributes directly to the theoretical extension of TAM, SDT, and self-efficacy by enhancing the literature on these topics. Eventually, this research produced the Model of E-Wallets Acceptance among Gen Z, which might aid the FinTech industry in developing strategies for future e-wallet rollouts and serve as a starting point for future R&D as the factors that influence the acceptance of e-wallets are explained by the model. The novel new model will have an impact on future research into the user acceptance of FinTech services.
5.2. Practical Contributions
Having a strategy for enabling the efficient rollout of e-wallets is crucial, particularly for the FinTech industry. Consequently, a data-driven strategy must be used to formulate an effective plan for an efficient rollout endeavor. Our findings, particularly the Model of E-Wallet Acceptance among Gen Z, might very well play a pivotal role in enabling the FinTech industries in formulating the strategic approach.
The e-wallet ecosystem should be seen as user-friendliness first and foremost, especially by users. This argument was supported by the considerable influence of SE on PU, β = 0.270, and PEU, β = 0.528. The technology involved, such as the mobile application, banking connection, and the transaction medium, should be user-friendly from the outset. Instead of establishing a new platform to enable e-wallet technology, such as a new mobile application, we propose that the existing platform be retrofitted to accommodate the e-wallet technology. As the technology being implemented is not new and the users have extensive experience with it, it would enable the users to have a better self-efficacy. This notion is also reinforced by the result that PEU is a significant determinant of PU, β = 0.307, and AT, β = 0.452. This implies that the ease of use of the e-wallet will have a significant impact on how Gen Z customers perceive the utility of the e-wallet initiative.
To boost the acceptance of e-wallets, the implementation of ER-related incentives such as discounts, subsidies, coupons, and tokens could be effective. As a result, practicality is affected by the need to offer such incentives. Our recommendation is based on the fact that our model demonstrated ER to be a predictor of Gen Z e-wallet PU, β = 0.520, and PEU, β = 0.430. This suggestion is also supporting the findings of earlier studies [38].We conclude that the implication of this study for the practical application of e-wallets is the need for FinTech-related industries to ensure the user-friendliness of the e-wallet ecosystem, to expand the existing platform to accommodate e-wallet facilities, and to use an appropriate incentive to accelerate the acceptance of e-wallets.
6. Limitations and Recommendations for Future Research
We would also like to note this study’s shortcomings. The variance of the model for BI is 89 percent, indicating that 11 percent of the variance cannot be explained by the model. This indicates that a tiny fraction of the factors that influence Gen Z’s acceptance of e-wallets require further investigation. The same is true for PU and PEU, which require an additional 13% and 20% exploration, respectively. This deficiency could be addressed by including more theory as an external variable [87]. Lastly, another limitation is that convenience sampling may have flaws that impair the generalizability of the results. Resultantly, there is a chance that our findings do not apply to other age brackets, although for Gen Z, we observe numerous similarities with prior studies. However, it has been confirmed that the sample size is sufficient for Covariance-based Structural Equation Model analysis [114]. However, future research could gain ground due to the limitations of our study. For instance, random sampling could be able to overcome the shortcomings of this study [135], but it would be difficult to ensure that the samples have actual experience with e-wallets and are within the appropriate age range to represent Gen Z. In addition to cognitive (PU and PEU), affective (SDT), and behavioral (BI) perspectives, psychomotor remains underdeveloped. Additional theories or models that might reflect psychomotor paradigm could be advantageous.
7. Conclusions
E-wallets are widely accepted by Gen Z, with their perception of the use of e-wallets governed by the extent to which they perceive e-wallets to be practical (PU–AT, β = 0.517, p < 0.0001) and convenient to use (PEU–AT, β = 0.452, p < 0.0001), as mediated by their attitude toward utilizing the technology (AT–BI, β = 0.941, p < 0.0001). ER (β = 0.520; β = 0.430) and SE (β = 0.270; β = 0.528) are the factors that influence Gen Z’s view toward e-wallet usefulness and ease to use perception about e-wallets. DMSE, on the other hand, solely has a function in determining PEU, β = 0.151. Which demonstrated that external reward and self-efficacy in e-wallet use are more influential than individual efficacy in using digital devices, based on the greater influence played by these factors at β values between 0.2 and 0.5 compared to the relatively low influence of DMSE at β values less than 0.2. To achieve an effective e-wallet strategy, the FinTech industries should emphasize the advantages afforded by this digital transaction and the familiarity of clients with the e-wallet. The attitude of Gen Z toward technology is also crucial for e-wallet widespread use and adoption, β = 0.941.
This study’s Model of E-Wallet Acceptance among Gen Z may be very impactful for FinTech ecosystem and academic research. To ensure the success of e-wallet rollout initiatives, notably among Gen Z, their favorable attitude toward technology must be ensured, the e-wallet ecosphere must be conducive and not burdensome for the users, and external rewards would encourage usage of the technology. Before e-wallets might be adopted by users, they must be perceived as valuable and easy to use. As our new model expands TAM to include the affective component of motivation as well as self-efficacy, it may serve as a catalyst for future study.
Conceptualization, M.S.R.; Data curation, M.S.R. and N.S.S.; Formal analysis, M.S.R. and N.S.S.; Funding acquisition, M.S.R.; Investigation, M.S.R. and N.S.S.; Methodology, A.M.A. and S.A.B.; Project administration, M.S.R.; Validation, A.M.A. and S.A.B.; Visualization, A.M.A., S.A.B. and N.S.S.; Writing—original draft, M.S.R.; Writing—review and editing, N.S.S., A.M.A. and S.A.B. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki.
Samples has given their consent right before participating in this study.
The data presented in this study are available on request from the corresponding author.
Authors would like to thank Ministry of Higher Education and Universiti Teknologi Malaysia for sponsoring this research through UTM Fundamental Research (UTMFR) grant with Project Number Q.J130000.2553.21H23.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Samples’ age distribution.
| Age (Years Old) | Frequency, f | Percent, % |
|---|---|---|
| 19 | 5 | 2.1 |
| 20 | 23 | 9.9 |
| 21 | 64 | 27.5 |
| 22 | 62 | 26.6 |
| 23 | 46 | 19.7 |
| 24 | 22 | 9.4 |
| 25 and older (maximum 28) | 11 | 4.7 |
Instrument CFA, item loading, CR, AVE, and Cronbach’s alpha.
| Theory/Model | Fit Indices | Variable | Item | Loading, λ | CR | AVE | Cronbach’s Alpha, α |
|---|---|---|---|---|---|---|---|
| SDT | χ2 = 171.368 |
IM | IM1 |
0.78 |
0.827 | 0.614 | 0.827 |
| IR | IR1 |
0.75 |
0.776 | 0.537 | 0.769 | ||
| ER | ER1 |
0.81 |
0.863 | 0.678 | 0.859 | ||
| A | A1 |
0.72 |
0.828 | 0.617 | 0.825 | ||
| Self–Efficacy | χ2 = 172.996 |
SE | SE1 |
0.74 |
0.927 | 0.647 | 0.927 |
| DMSE | DMSE1 |
0.73 |
0.890 | 0.618 | 0.887 | ||
| TAM | χ2 = 708.577 |
PU | PU1 |
0.87 |
0.954 | 0.778 | 0.954 |
| PEU | PEU1 |
0.91 |
0.964 | 0.817 | 0.963 | ||
| BI | BI1 |
0.87 |
0.973 | 0.818 | 0.973 | ||
| AT | AT1 |
0.88 |
0.905 | 0.706 | 0.894 |
Discriminant validity.
| IM | IR | ER | A | SE | DMSE | PU | PEU | BI | AT | |
|---|---|---|---|---|---|---|---|---|---|---|
| IM | ||||||||||
| IR | 1.007 | |||||||||
| ER | 0.903 | 1.073 | ||||||||
| A | 0.222 | 0.135 | 0.051 | |||||||
| SE | 0.641 | 0.755 | 0.742 | 0.077 | ||||||
| DMSE | 0.547 | 0.591 | 0.621 | 0.254 | 0.657 | |||||
| PU | 0.696 | 0.832 | 0.832 | 0.033 | 0.857 | 0.691 | ||||
| PEU | 0.662 | 0.785 | 0.784 | 0.043 | 0.856 | 0.676 | 0.888 | |||
| BI | 0.656 | 0.788 | 0.824 | 0.057 | 0.772 | 0.632 | 0.889 | 0.869 | ||
| AT | 0.644 | 0.833 | 0.836 | 0.029 | 0.767 | 0.634 | 0.868 | 0.871 | 0.905 |
Hypotheses testing based on Structural Model.
| Hypothesis | β | β | SE | CR | p | Result |
|---|---|---|---|---|---|---|
| H1: IM–PU | −0.194 | −0.212 | 0.163 | −1.303 | 0.193 | Rejected |
| H2: IR–PU | Not tested—discriminant validity issue | Rejected | ||||
| H3: ER–PU | 0.520 | 0.602 | 0.216 | 2.790 | 0.005 | Accepted |
| H4: A–PU | −0.028 | −0.021 | 0.036 | −0.598 | 0.550 | Rejected |
| H5: IM–PEU | −0.139 | −0.156 | 0.176 | −0.884 | 0.376 | Rejected |
| H6: IR–PEU | Not tested—discriminant validity issue | Rejected | ||||
| H7: ER–PEU | 0.430 | 0.512 | 0.219 | 2.335 | 0.020 | Accepted |
| H8: A–PEU | −0.034 | −0.027 | 0.041 | −0.661 | 0.509 | Rejected |
| H9: SE–PU | 0.270 | 0.283 | 0.079 | 3.577 | *** | Accepted |
| H10: SE–PEU | 0.528 | 0.570 | 0.081 | 7.011 | *** | Accepted |
| H11: DMSE–PU | 0.095 | 0.113 | 0.062 | 1.819 | 0.069 | Rejected |
| H12: DMSE–PEU | 0.151 | 0.183 | 0.071 | 2.571 | 0.010 | Accepted |
| H13: PU–AT | 0.517 | 0.502 | 0.076 | 6.634 | *** | Accepted |
| H14: PEU–AT | 0.452 | 0.426 | 0.072 | 5.938 | *** | Accepted |
| H15: PEU–PU | 0.307 | 0.298 | 0.085 | 3.512 | *** | Accepted |
| H16: AT–BI | 0.941 | 0.964 | 0.058 | 16.713 | *** | Accepted |
*** indicating p value less than 0.001.
Fit indices of direct, indirect, and mediation model.
| Model | Relationship | χ2 | AIC | PNFI |
|---|---|---|---|---|
| Direct | PU–AT–BI | 3.383 | 343.086 | 0.785 |
| Indirect | 2.936 | 449.543 | 0.802 | |
| Mediation | 2.751 | 427.149 | 0.799 | |
| Direct | 4.068 | 395.191 | 0.778 | |
| Indirect | PEU–AT–BI | 3.566 | 523.184 | 0.793 |
| Mediation | 3.491 | 512.958 | 0.806 |
Total effect, indirect effect, direct effect, and result of the mediation effect.
| Relationship | Total Effect | Indirect Effect | Direct Effect | Effect |
|---|---|---|---|---|
| PU–AT–BI | 0.465, p = 0.01 |
0.463, p = 0.01 |
0.000, |
Full mediation |
| PEU–AT–BI | 0.444, p = 0.01 |
0.429, p = 0.01 |
0.000, |
Full mediation |
LB: lower bounces, UB: upper bounces.
Appendix A
Items, variables, and references for the instrument.
| Variable | Item | Questionnaire Item | Reference |
|---|---|---|---|
| IM | IM1 | Because I think that using e-wallet is interesting | [ |
| IM3 | Because using e-wallet is fun | ||
| IM4 | Because I feel good when using e-wallet | ||
| IR | IR1 | Because I am using e-wallet for my own good | [ |
| IR2 | Because I think using e-wallet is good for me | ||
| IR3 | Using e-wallet is my personal decision | ||
| ER | ER1 | Because I am supposed to use e-wallet | [ |
| ER2 | Because using e-wallet is something that I have to do | ||
| ER4 | Because I feel that I have to do it | ||
| Amotivation | A1 | There may be good reasons to use e-wallet, but personally, I don’t see any | [ |
| A2 | I do use e-wallet, but I am not sure if it is worth it | ||
| A3 | I don’t know, I don’t see what using e-wallet brings me | ||
| SE | SE1 | I could complete the e-wallet-related task if no one is there to assist me by demonstrating how to use it. | [ |
| SE3 | I could complete the e-wallet-related task if I had only the mobile application manual as reference | ||
| SE4 | I could complete the e-wallet-related task if I had seen someone else using it before trying it myself | ||
| SE5 | I could complete the e-wallet-related task if I could be assisted if I had problem using it | ||
| SE6 | I could complete the e-wallet-related task if someone else had help me get started | ||
| SE7 | I could complete the e-wallet-related task if I have time to interact with it | ||
| SE8 | I could complete the e-wallet-related task if I had previously performed a nearly identical task | ||
| DMSE | DMSE1 | I am competent at using digital devices such as computer, laptop, smartphone, and tablet | [ |
| DMSE2 | I am competent at using digital devices that I am less familiar with | ||
| DMSE3 | If my friends or relatives wish to purchase digital devices such as a computer, laptop, smartphone, or tablet, I am able to advise them | ||
| DMSE5 | If there is a problem with a digital device, I think I can solve it. | ||
| DMSE6 | If my friends or relatives have a problem with a digital device, I can help them. | ||
| PU | PU1 | Using e-wallet enables me to complete my daily routine more quickly | [ |
| PU2 | Using e-wallet would improve my daily life performance | ||
| PU3 | Using e-wallet would increase my productivity | ||
| PU4 | Using e-wallet would enhance my effectiveness of my daily life | ||
| PU5 | Using e-wallet would make it easier for me to perform my daily task and process | ||
| PU6 | Overall, I feel e-wallet is beneficial | ||
| PEU | PEU1 | Learning to use e-wallet would be easy for me | [ |
| PEU2 | My interaction with e-wallet would be clear and understandable | ||
| PEU3 | It would be easy for me to become skillful at using e-wallet | ||
| PEU4 | I would find e-wallet to be flexible to interact with | ||
| PEU5 | I would find it easy to get e-wallet to do what I want it to do | ||
| PEU6 | I would find e-wallet easy to use | ||
| BI | BI1 | I intend to use e-wallet in the future | [ |
| BI2 | If I have access to e-wallet, I intend to use it | ||
| BI3 | I intend to use e-wallet in the future for daily purposes | ||
| BI4 | Assuming I have access to e-wallet, I intend to use it | ||
| BI5 | I will frequently use e-wallet in the future | ||
| BI7 | I would like to use many different forms of e-wallet for learning in the future | ||
| BI8 | It is worth to use e-wallet | ||
| BI9 | In the future, I intend to use e-wallet | ||
| AT | AT1 | I am enthusiastic about using the e-wallet in my daily life | [ |
| AT2 | I think it is a good idea to use the e-wallet for my daily life usage | ||
| AT3 | I like to use e-wallet | ||
| AT5 | I am looking forward to use e-wallet |
References
1. Prawira, M.F.A.; Susanto, E.; Goeltom, A.D.L.; Furqon, C. Developing Cashless Tourism from a Tourist Perspective: The Role of TAM and AMO Theory. J. Environ. Manag. Tour.; 2022; 13, pp. 2104-2112. [DOI: https://dx.doi.org/10.14505/jemt.v13.8(64).03] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35037002]
2. Rahman, M.; Ismail, I.; Bahri, S.; Rahman, M.K. An Empirical Analysis of Cashless Payment Systems for Business Transactions. J. Open Innov. Technol. Mark. Complex.; 2022; 8, 213. [DOI: https://dx.doi.org/10.3390/joitmc8040213]
3. Hassan, M.S.; Islam, M.A.; Sobhani, F.A.; Hassan, M.M.; Hassan, M.A. Patients’ Intention to Adopt Fintech Services: A Study on Bangladesh Healthcare Sector. Int. J. Environ. Res. Public Health; 2022; 19, 15302. [DOI: https://dx.doi.org/10.3390/ijerph192215302]
4. Chen, F.; Jiang, G. The Roles of FinTech with Perceived Mediators in Consumer Financial Satisfaction with Cashless Payments. Mathematics; 2022; 10, 3531. [DOI: https://dx.doi.org/10.3390/math10193531]
5. Alchuban, M.; Hamdan, A.; Fadhul, S.M. The Usage of Financial Technology Payments during the Pandemic of COVID-19; Hamdan, A.; Harraf, A.; Arora, P.; Alareeni, B.; Khamis Hamdan, R. Springer International Publishing: Cham, Switzerland, 2022; pp. 427-441. ISBN 978-3-030-99000-8
6. Riska, L.M.; Kholid, M.N.; Salsabilla, S. Perceived COVID-19 Risk and E-Wallet Adoption: An Empirical Evidence MSEs of Indonesia BT-The Implementation of Smart Technologies for Business Success and Sustainability: During COVID-19 Crises in Developing Countries; Hamdan, A.; Shoaib, H.M.; Alareeni, B.; Hamdan, R. Springer International Publishing: Cham, Switzerland, 2023; pp. 961-971. ISBN 978-3-031-10212-7
7. Astari, A.A.E.; Yasa, N.N.K.; Sukaatmadja, I.P.G.; Giantari, I.G.A.K. Integration of technology acceptance model (TAM) and theory of planned behavior (TPB): An e-wallet behavior with fear of COVID-19 as a moderator variable. Int. J. Data Netw. Sci.; 2022; 6, pp. 1427-1436. [DOI: https://dx.doi.org/10.5267/j.ijdns.2022.5.008]
8. Unting, D.J.; Abdullah, J.; Yap, M.N.K. Factors Affecting E-Wallet Usage in Sarawak. Proceedings of the 2022 International Conference on Digital Transformation and Intelligence (ICDI); Kuching, Malaysia, 1–2 December 2022; pp. 307-313.
9. Ojo, A.O.; Fawehinmi, O.; Ojo, O.T.; Arasanmi, C.; Tan, C.N.L. Consumer usage intention of electronic wallets during the COVID-19 pandemic in Malaysia. Cogent Bus. Manag.; 2022; 9, 2056964. [DOI: https://dx.doi.org/10.1080/23311975.2022.2056964]
10. Alsyouf, A.; Masa’deh, R.; Albugami, M.; Al-Bsheish, M.; Lutfi, A.; Alsubahi, N. Risk of fear and anxiety in utilising health app surveillance due to COVID-19: Gender differences analysis. Risks; 2021; 9, 179. [DOI: https://dx.doi.org/10.3390/risks9100179]
11. Alsyouf, A.; Lutfi, A.; Al-Bsheish, M.; Jarrar, M.; Al-Mugheed, K.; Almaiah, M.A.; Alhazmi, F.N.; Masa’deh, R.; Anshasi, R.J.; Ashour, A. Exposure Detection Applications Acceptance: The Case of COVID-19. Int. J. Environ. Res. Public Health; 2022; 19, 7307. [DOI: https://dx.doi.org/10.3390/ijerph19127307]
12. Tan, G.K.S. Citizens go digital: A discursive examination of digital payments in Singapore’s Smart Nation project. Urban Stud.; 2021; 59, pp. 2582-2598. [DOI: https://dx.doi.org/10.1177/00420980211039407]
13. Uesugi, S.; Matsuda, K.; Naruse, K.; Okada, H.; Morita, M. Is Cashless Going Right in Japan? An Observation Report. Proceedings of the 8th Multidisciplinary International Social Networks Conference; Association for Computing Machinery: New York, NY, USA, 2022; pp. 43-48. Available online: https://doi.org/10.1145/3504006.3504014 (accessed on 30 December 2022).
14. Feruś, A. The Development of Electronic Banking Services in Poland in the Era of the COVID-19 Pandemic using the Example of PKO Bank Polski. Folia Oeconomica Stetin.; 2022; 22, pp. 38-54. [DOI: https://dx.doi.org/10.2478/foli-2022-0018]
15. Khan, F.; Ateeq, S.; Ali, M.; Butt, N. Impact of COVID-19 on the drivers of cash-based online transactions and consumer behaviour: Evidence from a Muslim market. J. Islam. Mark.; 2023; 14, pp. 714-734. [DOI: https://dx.doi.org/10.1108/JIMA-09-2020-0265]
16. Srouji, J.; Torre, D. The global pandemic, laboratory of the cashless economy?. Int. J. Financ. Stud.; 2022; 10, 109. [DOI: https://dx.doi.org/10.3390/ijfs10040109]
17. Shaikh, O.; Ung, C.; Yang, D.; Chacon, F.A. Six Feet Apart: Online Payments During the COVID-19 Pandemic. Proc. ACM Hum. -Comput. Interact.; 2022; 6, pp. 1-33. [DOI: https://dx.doi.org/10.1145/3555218]
18. Aji, H.M.; Berakon, I.; Riza, A.F. The effects of subjective norm and knowledge about riba on intention to use e-money in Indonesia. J. Islam. Mark.; 2020; 12, pp. 1180-1196. [DOI: https://dx.doi.org/10.1108/JIMA-10-2019-0203]
19. Sikri, A.; Dalal, S.; Singh, N.; Le, D. Mapping of e-Wallets With Features. Cyber Security in Parallel and Distributed Computing; Le, D.; Kumar, R.; Mishra, B.; Khari, M.; Chatterjee, J. Scrivener Publishing LLC: Beverly, MA, USA, 2019; pp. 245-261. [DOI: https://dx.doi.org/10.1002/9781119488330.ch16]
20. Daragmeh, A.; Judit, S. Continuous Intention to Use E-Wallet in the Context of the COVID-19 Pandemic: Integrating the Health Belief Model (HBM) and Technology Continuous Theory (TCT). J. Open Innov. Technol. Mark. Complex.; 2021; 7, 132. [DOI: https://dx.doi.org/10.3390/joitmc7020132]
21. Hopalı, E.; Vayvay, Ö.; Kalender, Z.T.; Turhan, D.; Aysuna, C. How Do Mobile Wallets Improve Sustainability in Payment Services? A Comprehensive Literature Review. Sustainability; 2022; 14, 16541. [DOI: https://dx.doi.org/10.3390/su142416541]
22. Yang, M.; Al Mamun, A.; Mohiuddin, M.; Nawi, N.C.; Zainol, N.R. Cashless transactions: A study on intention and adoption of e-wallets. Sustainability; 2021; 13, 831. [DOI: https://dx.doi.org/10.3390/su13020831]
23. Zhavoronok, A.; Popelo, O.; Shchur, R.; Ostrovska, N.; Kordzaia, N. The Role of Digital Technologies in the Transformation of Regional Models of Households’ Financial Behavior in the Conditions of the National Innovative Economy Development. Ing. des Syst. D’information; 2022; 27, pp. 613-620. [DOI: https://dx.doi.org/10.18280/isi.270411]
24. Dubyna, M.; Popelo, O.; Kholiavko, N.; Zhavoronok, A.; Fedyshyn, M.; Yakushko, I. Mapping the Literature on Financial Behavior: A Bibliometric Analysis Using the VOSviewer Program. WSEAS Trans. Bus. Econ.; 2022; 19, pp. 231-246. [DOI: https://dx.doi.org/10.37394/23207.2022.19.22]
25. Michell, C.; Winarto, C.N.; Bestari, L.; Ramdhan, D.; Chowanda, A. Systematic Literature Review of E-Wallet: The Technology and Its Regulations in Indonesia. Proceedings of the 2022 International Conference on Information Technology Systems and Innovation (ICITSI); Bandung, Indonesia, 8–9 November 2022; pp. 64-69. [DOI: https://dx.doi.org/10.1109/ICITSI56531.2022.9970986]
26. Widhyastana, I.M.A.; Rachmawati, R. Digital Payment Application as a Cashless Utilization and its Benefit for the Community in Denpasar City. Int. J. Adv. Sci. Eng. Inf. Technol.; 2022; 12, pp. 1650-1656. [DOI: https://dx.doi.org/10.18517/ijaseit.12.4.12668]
27. Kaur, N.; Sahdev, S.L.; Chhabra, M.; Agarwal, S.M. FinTech Evolution to Revolution in India-From Minicorns to Soonicorns to Unicorns. Proceedings of the 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); Noida, India, 3–4 September 2021; pp. 1-6. [DOI: https://dx.doi.org/10.1109/ICRITO51393.2021.9596462]
28. Alam, M.M.; Awawdeh, A.E.; Muhamad, A.I. Bin Using e-wallet for business process development: Challenges and prospects in Malaysia. Bus. Process Manag. J.; 2021; 27, pp. 1142-1162. [DOI: https://dx.doi.org/10.1108/BPMJ-11-2020-0528]
29. Abu Daqar, M.A.M.; Arqawi, S.; Karsh, S.A. Fintech in the eyes of Millennials and Generation Z (the financial behavior and Fintech perception). Banks Bank Syst.; 2020; 15, pp. 20-28. [DOI: https://dx.doi.org/10.21511/bbs.15(3).2020.03]
30. Julião, J.; Ayllon, T.; Gaspar, M. Financial Inclusion through Digital Banking: The Case of Peru BT-Innovations in Industrial Engineering II; Machado, J.; Soares, F.; Trojanowska, J.; Ivanov, V.; Antosz, K.; Ren, Y.; Manupati, V.K. Springer International Publishing: Cham, Switzerland, 2023; pp. 294-304.
31. Aji, H.M.; Adawiyah, W.R. How e-wallets encourage excessive spending behavior among young adult consumers?. J. Asia Bus. Stud.; 2022; 16, pp. 868-884. [DOI: https://dx.doi.org/10.1108/JABS-01-2021-0025]
32. Kınış, F.; Tanova, C. Can I Trust My Phone to Replace My Wallet? The Determinants of E-Wallet Adoption in North Cyprus. J. Theor. Appl. Electron. Commer. Res.; 2022; 17, pp. 1696-1715. [DOI: https://dx.doi.org/10.3390/jtaer17040086]
33. Rosli, M.S.; Saleh, N.S.; Aris, B.; Ahmad, M.H.; Salleh, S.M. Ubiquitous hub for digital natives. Int. J. Emerg. Technol. Learn.; 2016; 11, pp. 29-34. [DOI: https://dx.doi.org/10.3991/ijet.v11i02.4993]
34. Aris, B.; Gharbaghi, A.; Ahmad, M.H.; Rosli, M.S. A check list for evaluating persuasive features of mathematics courseware. Int. Educ. Stud.; 2013; 6, pp. 125-134. [DOI: https://dx.doi.org/10.5539/ies.v6n9p125]
35. Baharom, M.M.; Atan, N.A.; Rosli, M.S.; Yusof, S.; Hamid, M.Z.A. Integration of science learning apps based on Inquiry Based Science Education (IBSE) in enhancing students Science Process Skills (SPS). Int. J. Interact. Mob. Technol.; 2020; 14, pp. 95-109. [DOI: https://dx.doi.org/10.3991/ijim.v14i09.11706]
36. Chalik, F.R.; Faturohman, T. Customer Satisfaction of E-wallet User: An Adoption of Information System Success Model. Quantitative Analysis of Social and Financial Market Development; Barnett, W.A.; Sergi, B.S. Emerald Publishing Limited: Bradford, UK, 2022; Volume 30, pp. 61-83. [DOI: https://dx.doi.org/10.1108/S1571-038620220000030005]
37. Abdul-Halim, N.A.; Vafaei-Zadeh, A.; Hanifah, H.; Teoh, A.P.; Nawaser, K. Understanding the determinants of e-wallet continuance usage intention in Malaysia. Qual. Quant.; 2022; 56, pp. 3413-3439. [DOI: https://dx.doi.org/10.1007/s11135-021-01276-7]
38. Lim, X.-J.; Ngew, P.; Cheah, J.-H.; Cham, T.H.; Liu, Y. Go digital: Can the money-gift function promote the use of e-wallet apps?. Internet Res.; 2022; 32, pp. 1806-1831. [DOI: https://dx.doi.org/10.1108/INTR-06-2021-0406]
39. Tang, M.B.; Dieo, B.A.; Suhaimi, M.K.A.M.; Andam, J.L.A. the Emergence of E-Wallet in Sarawak: Factors Influencing the Adoption of Sarawak Pay. Int. J. Bus. Soc.; 2022; 23, pp. 1423-1442. [DOI: https://dx.doi.org/10.33736/ijbs.5172.2022]
40. Ikhsan, R.B.; Sari, C.L.; Fernando, E.; Wijaya, L.; Bangapadang, S.; Sangkereng, I. The Rapid Adoption of E-wallet: An empirical study. Proceedings of the 2022 10th International Conference on Cyber and IT Service Management (CITSM); Yogyakarta, Indonesia, 20–21 September 2022; pp. 1-5. [DOI: https://dx.doi.org/10.1109/citsm56380.2022.9935931]
41. Bohari, S.A.; Abdul-Rahim, R.; Aman, A. Role of comparative economic benefits on intention to use e-wallet: The case in Malaysia. Int. J. Electron. Financ.; 2022; 11, pp. 364-382. [DOI: https://dx.doi.org/10.1504/IJEF.2022.126476]
42. Abbasi, G.A.; Sandran, T.; Ganesan, Y.; Iranmanesh, M. Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA. Technol. Soc.; 2022; 68, 101937. [DOI: https://dx.doi.org/10.1016/j.techsoc.2022.101937]
43. Prima Johan, A.; Lukviarman, N.; Eka Putra, R. Continuous intention to use e-wallets in Indonesia: The impact of e-wallets features. Innov. Mark.; 2022; 18, pp. 74-85. [DOI: https://dx.doi.org/10.21511/im.18(4).2022.07]
44. Chauhan, V.; Yadav, R.; Choudhary, V. Adoption of electronic banking services in India: An extension of UTAUT2 model. J. Financ. Serv. Mark.; 2022; 27, pp. 27-40. [DOI: https://dx.doi.org/10.1057/s41264-021-00095-z]
45. Esawe, A.T. Understanding mobile e-wallet consumers’ intentions and user behavior. Spanish J. Mark.-ESIC; 2022; 26, pp. 363-384. [DOI: https://dx.doi.org/10.1108/SJME-05-2022-0105]
46. Nugroho, A.; Siagian, H.; Oktavio, A.; Tarigan, Z.J.H. The effect of e-WOM on customer satisfaction through ease of use, perceived usefulness and e-wallet payment. Int. J. Data Netw. Sci.; 2023; 7, pp. 153-162. [DOI: https://dx.doi.org/10.5267/j.ijdns.2022.11.007]
47. Yaakop, A.Y.; Shi, Y.P.; Foster, B.; Saputra, J. Investigating e-wallet adoption of COVID19 intra-period among Malaysian youths’: Integrated task-technology fit and technology acceptance model framework. Int. J. Data Netw. Sci.; 2021; 5, pp. 295-302. [DOI: https://dx.doi.org/10.5267/j.ijdns.2021.6.004]
48. Nur, T.; Joviando, J. Determination of E-Wallet Usage Intention: Extending the TAM Model with Self Efficacy. Proceedings of the 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS); Makasar, Indonesia, 25–26 October 2021; [DOI: https://dx.doi.org/10.1109/ICORIS52787.2021.9649568]
49. Osman, S.; Jabaruddin, N.; Zon, A.S.; Jifridin, A.A.; Zolkepli, A.K. Factors influencing the use of E-wallet among millennium tourist. J. Inf. Technol. Manag.; 2021; 13, pp. 70-81. [DOI: https://dx.doi.org/10.22059/jitm.2021.83114]
50. Phan, T.N.; Ho, T.V.; Le-hoang, P.V. Factors Affecting the Behavioral Intention and Behavior of Using E–Wallets of Youth in Vietnam. J. Asian Financ. Econ. Bus.; 2020; 7, pp. 295-302. [DOI: https://dx.doi.org/10.13106/jafeb.2020.vol7.n10.295]
51. Lee, Y.K. Impacts of digital technostress and digital technology self-efficacy on fintech usage intention of Chinese gen Z consumers. Sustainability; 2021; 13, 5077. [DOI: https://dx.doi.org/10.3390/su13095077]
52. Mahmood, A.; Imran, M.; Adil, K. Modeling Individual Beliefs to Transfigure Technology Readiness into Technology Acceptance in Financial Institutions. SAGE Open; 2023; 13, 21582440221149718. [DOI: https://dx.doi.org/10.1177/21582440221149718]
53. Rafique, H.; Ul Islam, Z.; Shamim, A. Acceptance of e-learning technology by government school teachers: Application of extended technology acceptance model. Interact. Learn. Environ.; 2023; pp. 1-19. [DOI: https://dx.doi.org/10.1080/10494820.2022.2164783]
54. Bagdi, H.; Bulsara, H.P. Understanding the role of perceived enjoyment, self-efficacy and system accessibility: Digital natives’ online learning intentions. J. Appl. Res. High. Educ.; 2023; ahead-of-print [DOI: https://dx.doi.org/10.1108/JARHE-09-2022-0302]
55. Jokisch, M.R.; Schmidt, L.I.; Doh, M. Acceptance of digital health services among older adults: Findings on perceived usefulness, self-e cacy, privacy concerns, ICT knowledge, and support seeking. Front. Public Health; 2022; 10, 1073756. [DOI: https://dx.doi.org/10.3389/fpubh.2022.1073756]
56. Kim, S.; Jang, S.; Choi, W.; Youn, C.; Lee, Y. Contactless service encounters among Millennials and Generation Z: The effects of Millennials and Gen Z characteristics on technology self-efficacy and preference for contactless service. J. Res. Interact. Mark.; 2022; 16, pp. 82-100. [DOI: https://dx.doi.org/10.1108/JRIM-01-2021-0020]
57. Nayak, B.; Bhattacharyya, S.S.; Kumar, S.; Jumnani, R.K. Exploring the factors influencing adoption of health-care wearables among generation Z consumers in India. J. Information, Commun. Ethics Soc.; 2022; 20, pp. 150-174. [DOI: https://dx.doi.org/10.1108/JICES-07-2021-0072]
58. Katja, K.; Vivien, M.; Geok, K.; Barbara, L. Cyberloafing among Gen Z students: The role of norms, moral disengagement, multitasking self - efficacy, and psychological outcomes. Eur. J. Psychol. Educ.; 2022; [DOI: https://dx.doi.org/10.1007/s10212-022-00617-w]
59. Sybirianska, Y.; Dyba, M.; Britchenko, I.; Ivashchenko, A.; Vasylyshen, Y.; Polishchuk, Y. Fintech platforms in sme’s financing: Eu experience and ways of their application in Ukraine. Invest. Manag. Financ. Innov.; 2018; 15, pp. 83-96. [DOI: https://dx.doi.org/10.21511/imfi.15(3).2018.07]
60. Priporas, C.V.; Stylos, N.; Fotiadis, A.K. Generation Z consumers’ expectations of interactions in smart retailing: A future agenda. Comput. Human Behav.; 2017; 77, pp. 374-381. [DOI: https://dx.doi.org/10.1016/j.chb.2017.01.058]
61. Ghandour, A.; Al-Srehan, H.; Almutairi, A. Analysis of Demand and Supply for Mobile Payments in the UAE during COVID-19. J. Risk Financ. Manag.; 2023; 16, 59. [DOI: https://dx.doi.org/10.3390/jrfm16020059]
62. Wang, C.; Riyu, P.; Xiaoyang, W.; Yilin, T.; Linkang, X.; Cyrus, S.H.; Roger, C.H. Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. Int. J. Environ. Res. Public Health; 2020; 17, 1729. [DOI: https://dx.doi.org/10.3390/ijerph17051729] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32155789]
63. Xiong, J.; Lipsitz, O.; Nasri, F.; Lui, L.M.W.; Gill, H.; Phan, L.; Chen-Li, D.; Iacobucci, M.; Ho, R.; Majeed, A. et al. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. J. Affect. Disord.; 2020; 277, pp. 55-64. [DOI: https://dx.doi.org/10.1016/j.jad.2020.08.001]
64. Rosli, M.S.; Saleh, N.S.; Ali, A.; Abu Bakar, S. Self-Determination Theory and Online Learning in University: Advancements, Future Direction and Research Gaps. Sustainability; 2022; 14, 14655. [DOI: https://dx.doi.org/10.3390/su142114655]
65. Patricia Aguilera-Hermida, A. College students’ use and acceptance of emergency online learning due to COVID-19. Int. J. Educ. Res. Open; 2020; 1, 100011. [DOI: https://dx.doi.org/10.1016/j.ijedro.2020.100011] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35059662]
66. Singh, N.; Sinha, N.; Liébana-cabanillas, F.J. Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. Int. J. Inf. Manag.; 2020; 50, pp. 191-205. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2019.05.022]
67. Ayesha, S.; Shafiq, M.; Kakria, P. Investigating acceptance of telemedicine services through an extended technology acceptance model ( TAM ). Technol. Soc.; 2020; 60, 101212. [DOI: https://dx.doi.org/10.1016/j.techsoc.2019.101212]
68. Deci, E.L.; Connell, J.P.; Ryan, R.M. Self-Determination in a Work Organization. J. Appl. Psychol.; 1989; 74, pp. 580-590. [DOI: https://dx.doi.org/10.1037/0021-9010.74.4.580]
69. Bandura, A. Self-Efficacy: Toward A Unifying Theory of Behavioral Change. Psychol. Rev.; 1977; 84, pp. 191-215. [DOI: https://dx.doi.org/10.1037/0033-295X.84.2.191]
70. Ryan, R.M.; Deci, E.L. Brick by Brick: The Origins, Development, and Future of Self-Determination Theory. Adv. Motiv. Sci.; 2019; 6, pp. 111-156. [DOI: https://dx.doi.org/10.1016/bs.adms.2019.01.001]
71. Tian, Y.; Chan, T.J.; Suki, N.M.; Kasim, M.A. Moderating Role of Perceived Trust and Perceived Service Quality on Consumers’ Use Behavior of Alipay e-wallet System: The Perspectives of Technology Acceptance Model and Theory of Planned Behavior. Hum. Behav. Emerg. Technol.; 2023; 2023, 5276406. [DOI: https://dx.doi.org/10.1155/2023/5276406]
72. Ryan, R.M.; Deci, E.L. Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemp. Educ. Psychol.; 2020; 61, 101860. [DOI: https://dx.doi.org/10.1016/j.cedpsych.2020.101860]
73. Hao, Y.; Lan, Y. Research and practice of flipped classroom based on mobile applications in local universities from the perspective of self-determination theory. Front. Psychol.; 2023; 13, 963226. [DOI: https://dx.doi.org/10.3389/fpsyg.2022.963226] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36698595]
74. Corbin, C.M.; Downer, J.T.; Lowenstein, A.E.; Brown, J.L. Reconsidering teachers’ basic psychological needs in relation to psychological functioning across an academic year. Teach. Teach. Educ.; 2023; 123, 103989. [DOI: https://dx.doi.org/10.1016/j.tate.2022.103989]
75. Chen, Y.; Zhao, S. Understanding Chinese EFL Learners’ Acceptance of Gamified Vocabulary Learning Apps: An Integration of Self-Determination Theory and Technology Acceptance Model. Sustainability; 2022; 14, 11288. [DOI: https://dx.doi.org/10.3390/su141811288]
76. Panisoara, I.O.; Lazar, I.; Panisoara, G.; Chirca, R.; Ursu, A.S. Motivation and Continuance Intention towards Online Instruction among Teachers during the COVID-19 Pandemic: The Mediating Effect of Burnout and Technostress. Int. J. Environ. Res. Public Health; 2020; 17, 8002. [DOI: https://dx.doi.org/10.3390/ijerph17218002] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33143180]
77. Handoko, B.L.; Karmawan, I.G.M.; Meliana, L. Factors Influenced User Interest in Payment Transaction of ShopeePay Digital Wallet Application. Proceedings of the 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS); Prapat, Indonesia, 8–9 October 2022; [DOI: https://dx.doi.org/10.1109/ICORIS56080.2022.10031472]
78. Wong, H.W.; Kwok, A.O.J. Going Cashless? How Has COVID-19 Affected the Intention to Use E-wallets? BT-Cross-Cultural Design. Applications in Business, Communication, Health, Well-Being, and Inclusiveness; Rau, P.-L.P. Springer International Publishing: Cham, Switzerland, 2022; pp. 265-276.
79. Standage, M.; Duda, J.L.; Ntoumanis, N. A test of self-determination theory in school physical education. Br. J. Educ. Psychol.; 2005; 75, pp. 411-433. [DOI: https://dx.doi.org/10.1348/000709904X22359] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16238874]
80. Lopes, S.; Sabino, A.; Dias, P.C.; Rodrigues, A.; Chambel, M.J.; Cesário, F. Through the Lens of Workers’ Motivation: Does It Relate to Work–Family Relationship Perceptions?. Sustainability; 2022; 14, 16117. [DOI: https://dx.doi.org/10.3390/su142316117]
81. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q.; 1989; 13, pp. 319-340. [DOI: https://dx.doi.org/10.2307/249008]
82. Rahmayanti, P.L.D.; Widagda, I.G.N.J.A.; Yasa, N.N.K.; Giantari, I.G.A.K.; Martaleni,; Sakti, D.P.B.; Suwitho,; Anggreni, P. Integration of technology acceptance model and theory of reasoned action in predicting e-wallet continuous usage intentions. Int. J. Data Netw. Sci.; 2021; 5, pp. 649-658. [DOI: https://dx.doi.org/10.5267/j.ijdns.2021.8.002]
83. Ariffin, S.K.; Abd Rahman, M.F.R.; Muhammad, A.M.; Zhang, Q. Understanding the consumer’s intention to use the e-wallet services. Spanish J. Mark.-ESIC; 2021; 25, pp. 446-461. [DOI: https://dx.doi.org/10.1108/SJME-07-2021-0138]
84. Bandura, A. Self-efficacy mechanism in human agency. Am. Psychol.; 1982; 37, pp. 122-147. [DOI: https://dx.doi.org/10.1037/0003-066X.37.2.122]
85. Bandura, A. Perceived Self-Efficacy in Cognitive Development and Functioning. Educ. Psychol.; 1993; 28, pp. 117-148. [DOI: https://dx.doi.org/10.1207/s15326985ep2802_3]
86. Yalley, A.A. Customer readiness to co-production of mobile banking services: A customer-only co-production perspective. J. Financ. Serv. Mark.; 2022; 27, pp. 81-95. [DOI: https://dx.doi.org/10.1057/s41264-021-00105-0]
87. Rosli, M.S.; Saleh, N.S.; Ali, A.; Abu Bakar, S.; Mohd Tahir, L. A Systematic Review of the Technology Acceptance Model for the Sustainability of Higher Education during the COVID-19 Pandemic and Identified Research Gaps. Sustainability; 2022; 14, 11389. [DOI: https://dx.doi.org/10.3390/su141811389]
88. Abdullah, F.; Ward, R. Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Comput. Human Behav.; 2016; 56, pp. 238-256. [DOI: https://dx.doi.org/10.1016/j.chb.2015.11.036]
89. Marangunić, N.; Granić, A. Technology acceptance model: A literature review from 1986 to 2013. Univers. Access Inf. Soc.; 2015; 14, pp. 81-95. [DOI: https://dx.doi.org/10.1007/s10209-014-0348-1]
90. Compeau, D.R.; Higgins, C.A. Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Q.; 1995; 19, pp. 189-211. [DOI: https://dx.doi.org/10.2307/249688]
91. Hammer, M.; Scheiter, K.; Stürmer, K. New technology, new role of parents: How parents’ beliefs and behavior affect students’ digital media self-efficacy. Comput. Human Behav.; 2021; 116, 106642. [DOI: https://dx.doi.org/10.1016/j.chb.2020.106642]
92. Pumptow, M.; Brahm, T. Students’ Digital Media Self-Efficacy and Its Importance for Higher Education Institutions: Development and Validation of a Survey Instrument. Technol. Knowl. Learn.; 2021; 26, pp. 555-575. [DOI: https://dx.doi.org/10.1007/s10758-020-09463-5]
93. So, H.-J.; Shin, S.; Xiong, Y.; Kim, H. Parental involvement in digital home-based learning during COVID-19: An exploratory study with Korean parents. Educ. Psychol.; 2022; 42, pp. 1301-1321. [DOI: https://dx.doi.org/10.1080/01443410.2022.2078479]
94. Davis, F.D. User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. Int. J. Man. Mach. Stud.; 1993; 38, pp. 475-487. [DOI: https://dx.doi.org/10.1006/imms.1993.1022]
95. Lutfi, A. Understanding Cloud Based Enterprise Resource Planning Adoption among SMES in Jordan. J. Theor. Appl. Inf. Technol.; 2021; 99, pp. 5944-5953.
96. Shahzad, A.; Zahrullail, N.; Akbar, A.; Mohelska, H.; Hussain, A. COVID-19’s Impact on Fintech Adoption: Behavioral Intention to Use the Financial Portal. J. Risk Financ. Manag.; 2022; 15, 428. [DOI: https://dx.doi.org/10.3390/jrfm15100428]
97. Baber, H.; Baki Billah, N.M. Fintech and Islamic Banks-an integrative model approach to predict the intentions. Rev. Appl. Socio-Economic Res.; 2022; 24, pp. 24-45. [DOI: https://dx.doi.org/10.54609/reaser.v24i2.215]
98. Kasemharuethaisuk, H.; Samanchuen, T. Factors Influencing Behavior Intention in Digital Investment Services of Mutual Fund Distributors Adoption in Thailand. Sustainability; 2023; 15, 2279. [DOI: https://dx.doi.org/10.3390/su15032279]
99. Rosli, M.S.; Saleh, N.S. Technology enhanced learning acceptance among university students during Covid-19: Integrating the full spectrum of Self-Determination Theory and self-efficacy into the Technology Acceptance Model. Curr. Psychol.; 2022; [DOI: https://dx.doi.org/10.1007/s12144-022-02996-1]
100. Warchlewska, A.; Warchlewska, A. Comparative analysis of Poland and selected countries in terms of household financial behaviour during the COVID-19 pandemic Introduction The COVID-19 pandemic has frozen some economic sectors and worsened the financial situation of societies worldwide. Equilib. J. Ekon. Syariah; 2021; 16, pp. 578-593.
101. Al-Badi, A.H.; Govindaluri, S.M.; Sharma, S.K.; Khan, A.I. Global and local perspective on the usage of mobile wallet. Int. J. Bus. Inf. Syst.; 2022; 39, pp. 550-568. [DOI: https://dx.doi.org/10.1504/IJBIS.2022.122883]
102. Senali, M.G.; Iranmanesh, M.; Ismail, F.N.; Rahim, N.F.A.; Khoshkam, M.; Mirzaei, M. Determinants of Intention to Use e-Wallet: Personal Innovativeness and Propensity to Trust as Moderators. Int. J. Human–Comput. Interact.; 2022; pp. 1-13. [DOI: https://dx.doi.org/10.1080/10447318.2022.2076309]
103. Mascret, N.; Temprado, J.J. Acceptance of a Mobile Telepresence Robot, before Use, to Remotely Supervise Older Adults’ Adapted Physical Activity. Int. J. Environ. Res. Public Health; 2023; 20, 3012. [DOI: https://dx.doi.org/10.3390/ijerph20043012] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36833706]
104. Voicu, M.C.; Muntean, M. Factors That Influence Mobile Learning among University Students in Romania. Electron.; 2023; 12, 938. [DOI: https://dx.doi.org/10.3390/electronics12040938]
105. Venkatesh, V. Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Inf. Syst. Res.; 2000; 11, pp. 342-365. [DOI: https://dx.doi.org/10.1287/isre.11.4.342.11872]
106. Okonkwo, C.W.; Amusa, L.B.; Twinomurinzi, H.; Fosso Wamba, S. Mobile wallets in cash-based economies during COVID-19. Ind. Manag. Data Syst.; 2022; 123, pp. 653-671. [DOI: https://dx.doi.org/10.1108/IMDS-01-2022-0029]
107. Ly, B.; Ly, R.; Hor, S. Zoom classrooms and adoption behavior among Cambodian students. Comput. Hum. Behav. Reports; 2023; 9, 100266. [DOI: https://dx.doi.org/10.1016/j.chbr.2022.100266]
108. Luarn, P.; Lin, H.H. Toward an understanding of the behavioral intention to use mobile banking. Comput. Human Behav.; 2005; 21, pp. 873-891. [DOI: https://dx.doi.org/10.1016/j.chb.2004.03.003]
109. Dina, H. Bassiouni; Chris Hackley ‘Generation Z’ children’s adaptation to digital consumer culture: A critical literature review. J. Cust. Behav.; 2014; 9, pp. 37-53.
110. Memon, I.A.; Nair, S.; Jakhiya, M. How Ready the GEN-Z is to Adopt FinTech ?. Proceedings of the 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE); Dubai, United Arab Emirates, 17–18 March 2021; pp. 565-570. [DOI: https://dx.doi.org/10.1109/ICCIKE51210.2021.9410747]
111. Hair, J.; W.C, B.; Babin, B. Multivariate Data Analysis: A Global Perspective; Pearson Education: Upper Saddle River, NJ, USA, 2010.
112. Kline, R.B. Principles and Practice of Structural Equation Modeling; 3rd ed. Methodology in the Social Sciences.; Guilford Press: New York, NY, USA, 2011.
113. Roscoe, J.T. Fundamental Research Statistics for the Behavioral Sciences; Editors’ Series in Marketing; Holt, Rinehart and Winston: New York, NY, USA, 1975; Available online: https://books.google.com.my/books?id=Fe8vAAAAMAAJ (accessed on 27 December 2022).
114. Ali Memon, M.; Ting, H.; Cheah, J.-H.; Thurasamy, R.; Chuah, F.; Huei Cham, T. Sample Size for Survey Research: Review and Recommendations. J. Appl. Struct. Equ. Model.; 2020; 4, pp. 2590-4221.
115. Fornell, C.; Larcker, D. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res.; 1981; 18, pp. 39-50. [DOI: https://dx.doi.org/10.1177/002224378101800104]
116. Rosli, M.S.; Saleh, N.S.; Alshammari, S.H.; Ibrahim, M.M.; Atan, A.S.; Atan, N.A. Improving Questionnaire Reliability using Construct Reliability for Researches in Educational Technology. Int. J. Interact. Mob. Technol.; 2021; 15, pp. 109-116. [DOI: https://dx.doi.org/10.3991/ijim.v15i04.20199]
117. Sudbury-Riley, L.; FitzPatrick, M.; Schulz, P.J. Exploring the measurement properties of the eHealth literacy scale (eHEALS) among baby boomers: A multinational test of measurement invariance. J. Med. Internet Res.; 2017; 19, e53. [DOI: https://dx.doi.org/10.2196/jmir.5998]
118. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model.; 1999; 6, pp. 1-55. [DOI: https://dx.doi.org/10.1080/10705519909540118]
119. Chamorro-Koc, M.; Peake, J.; Meek, A.; Manimont, G. Self-efficacy and trust in consumers’ use of health-technologies devices for sports. Heliyon; 2021; 7, e07794. [DOI: https://dx.doi.org/10.1016/j.heliyon.2021.e07794]
120. Teo, T.; Dai, H.M. The role of time in the acceptance of MOOCs among Chinese university students. Interact. Learn. Environ.; 2019; 30, pp. 651-664. [DOI: https://dx.doi.org/10.1080/10494820.2019.1674889]
121. Zain, F.M.; Sailin, S.N. Factors of using e-learning in higher education and its impact on student learning. Int. J. Eval. Res. Educ.; 2023; 12, pp. 377-385. [DOI: https://dx.doi.org/10.11591/ijere.v12i1.23912]
122. Abdullah, F.; Ward, R.; Ahmed, E. Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Comput. Human Behav.; 2016; 63, pp. 75-90. [DOI: https://dx.doi.org/10.1016/j.chb.2016.05.014]
123. Kelly, S.; Kaye, S.A.; Oviedo-Trespalacios, O. What factors contribute to the acceptance of artificial intelligence? A systematic review. Telemat. Inform.; 2023; 77, 101925. [DOI: https://dx.doi.org/10.1016/j.tele.2022.101925]
124. Kao, W.K.; Huang, Y.S. (Sandy) Service robots in full- and limited-service restaurants: Extending technology acceptance model. J. Hosp. Tour. Manag.; 2023; 54, pp. 10-21. [DOI: https://dx.doi.org/10.1016/j.jhtm.2022.11.006]
125. Huang, Y.C.; Li, L.N.; Lee, H.Y.; Browning, M.H.E.M.; Yu, C.P. Surfing in virtual reality: An application of extended technology acceptance model with flow theory. Comput. Hum. Behav. Reports; 2023; 9, 100252. [DOI: https://dx.doi.org/10.1016/j.chbr.2022.100252]
126. Chueh, H.E.; Huang, D.H. Usage intention model of digital assessment systems. J. Bus. Res.; 2023; 156, 113469. [DOI: https://dx.doi.org/10.1016/j.jbusres.2022.113469]
127. Alhasan, A.; Hussein, M.H.; Audah, L.; Al-Sharaa, A.; Ibrahim, I.; Mahmoud, M.A. A case study to examine undergraduate students’ intention to use internet of things (IoT) services in the smart classroom. Educ. Inf. Technol.; 2023; [DOI: https://dx.doi.org/10.1007/s10639-022-11537-z]
128. Linardatos, G.; Apostolou, D. Investigating high school students’ perception about digital comics creation in the classroom. Educ. Inf. Technol.; 2023; [DOI: https://dx.doi.org/10.1007/s10639-023-11581-3]
129. Park, D.Y.; Kim, H. Determinants of Intentions to Use Digital Mental Healthcare Content among University Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot. Sustainability; 2023; 15, 872. [DOI: https://dx.doi.org/10.3390/su15010872]
130. Raub, L.A.; Shukor, N.A.; Arshad, M.Y.; Rosli, M.S. An integrated model to implement contextual learning with virtual learning environment for promoting higher order thinking skills in Malaysian secondary schools. Int. Educ. Stud.; 2015; 8, pp. 41-46. [DOI: https://dx.doi.org/10.5539/ies.v8n13p41]
131. Howard, J.L.; Bureau, J.; Guay, F.; Chong, J.X.Y.; Ryan, R.M. Student Motivation and Associated Outcomes: A Meta-Analysis From Self-Determination Theory. Perspect. Psychol. Sci.; 2021; 16, pp. 1300-1323. [DOI: https://dx.doi.org/10.1177/1745691620966789] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33593153]
132. Chahal, J.; Rani, N. Exploring the acceptance for e-learning among higher education students in India: Combining technology acceptance model with external variables. J. Comput. High. Educ.; 2022; 34, pp. 844-867. [DOI: https://dx.doi.org/10.1007/s12528-022-09327-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35668837]
133. Songkram, N.; Osuwan, H. Applying the Technology Acceptance Model to Elucidate K-12 Teachers’ Use of Digital Learning Platforms in Thailand during the COVID-19 Pandemic. Sustainability; 2022; 14, 6027. [DOI: https://dx.doi.org/10.3390/su14106027]
134. Hu, Y.; Su, C.Y.; Fu, A. Factors Influencing Younger Adolescents’ Intention to Use Game-Based Programming Learning: A Multigroup Analysis. Educ. Inf. Technol.; 2022; 27, pp. 8203-8233. [DOI: https://dx.doi.org/10.1007/s10639-022-10973-1]
135. Rosli, M.S.; Aris, B.; Ahmad, M.H. Online intellectual transformation system. Contemp. Eng. Sci.; 2015; 8, pp. 39-47. [DOI: https://dx.doi.org/10.12988/ces.2015.412254]
136. Guay, F.; Vallerand, R.J.; Blanchard, C. On the assessment of situational intrinsic and extrinsic motivation: The Situational Motivation Scale (SIMS). Motiv. Emot.; 2000; 24, pp. 175-213. [DOI: https://dx.doi.org/10.1023/A:1005614228250]
137. Huang, F.; Teo, T.; Zhou, M. Chinese students’ intentions to use the Internet-based technology for learning. Educ. Technol. Res. Dev.; 2020; 68, pp. 575-591. [DOI: https://dx.doi.org/10.1007/s11423-019-09695-y]
138. Sivo, S.A.; Ku, C.H.; Acharya, P. Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australas. J. Educ. Technol.; 2018; 34, pp. 72-91. [DOI: https://dx.doi.org/10.14742/ajet.2806]
139. Teo, T.; Zhou, M.; Fan, A.C.W.; Huang, F. Factors that influence university students’ intention to use Moodle: A study in Macau. Educ. Technol. Res. Dev.; 2019; 67, pp. 749-766. [DOI: https://dx.doi.org/10.1007/s11423-019-09650-x]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Nor Shela Saleh 2 ; Azlah Md Ali 3 ; Suaibah Abu Bakar 3 1 School of Education, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
2 Department of Social Science, Centre for General Studies and Co-Curricular, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia
3 School of Human Resource Development and Psychology, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia