Technology readiness and UTAUT2 in e-wallet adoption in a developing country

Technology readiness

Technology readiness is defined as a person’s propensity to embrace and use specific new technology for accomplishing life and work-related goals (Parasuraman, 2000, p. 308). It consists of four dimensions which can be categorized into two scopes, namely: positive enablers (optimism and innovativeness) and negative inhibitors (discomfort and insecurity) to measure human reactions (Parasuraman, 2000a). Parasuraman (2000, p. 311) describes optimism as “a positive view of technology”; innovativeness as “A tendency to be a technology pioneer and thought leader”; discomfort as “a perceived lack of control over technology and a feeling of being overwhelmed by it”; lastly, insecurity as “distrust of technology and skepticism about its ability to work properly”.

Back in 2005, a new model named “TRAM” which is an integrated model of Technology Acceptance Model (TAM) and technology readiness was presented by Lin, Shih, Sher, & Wang (2005) to carry out a research regarding e-service adoption in Taiwan. The reason that led the authors to the combination is that TAM is system-specific whereas technology readiness is individual-specific, and therefore, they believed that they are interrelated. Extant research has incorporated technology readiness and technology adoption model to explain users’ readiness and perceptions toward financial technology (Walczuch, Lemmink, & Streukens, 2007), electronic human resource management (Erdoǧmu & Esen, 2011), electronic health record system (Godoe & Johansen, 2012; Kuo, Liu, & Ma, 2013), web-based attendance management system (Nugroho & Fajar, 2017) and mobile payment system (Rahman, Taghizadeh, Ramayah, & Alam, 2017).

Unified Theory of Acceptance and Use of Technology (UTAUT2)

UTAUT2 developed by Venkatesh, Thong, & Xu (2012), is the extension of UTAUT (Venkatesh, Morris, Davis, & Davis, 2003). UTAUT2 consists of seven core constructs namely performance expectancy, effort expectancy, facilitating conditions, social influence, and three additional constructs: price value, habit, and hedonic motivation to explain one’s perceptions toward technology from the consumer view (Venkatesh et al., 2012). Performance expectancy is defined as “the degree to which a person believes that using the system will help him or her to attain gains in job performance” (Venkatesh et al., 2003, p. 447); effort expectancy is defined as “the degree of ease associated with the use of the system which reflects user perception of the difficulty when using a specific technology (Venkatesh et al., 2003, p. 405); facilitating conditions is defined as “the degree to which a person believes that an organisational and technical infrastructure exists to support the use of the system” (Venkatesh et al., 2003, p. 453); social influence is defined as “the degree to which a person perceives that important others believe he or she should use the new system” (Venkatesh et al., 2003, p. 451); hedonic motivation is defined as “the pleasure or fun derived from technology use” (Brown & Venkatesh, 2005; Venkatesh et al., 2012, p. 161); lastly, price value is defined as “the trade-offs between the perceived benefits and cost of using a specific technology” (Venkatesh et al., 2012, p.161).

Compared to UTAUT, the improvements made in UTAUT2 have established a significant enhancement in describing the variability of behavioural intention (18% increase) and use behaviour (12% increase) (Venkatesh et al., 2012). It can be said that UTAUT2 consists of the essences of the recognized theories. UTAUT3 is not considered in this study as the additional construct of UTAUT3: personal innovativeness is already involved in the technology readiness model.

Research framework and hypothesis development

The proposed research framework is portrayed in Figure 1. The research framework proposed that the personality traits of technology readiness are interrelated with performance expectancy and effort expectancy from UTAUT2. Building on the literature of the TRAM model, this study postulated the following hypotheses:

Figure 1. Research framework.

H1: Optimism positively influences performance expectancy (a) and effort expectancy (b) of e-wallets among Malaysians.

H2: Innovativeness positively influences performance expectancy (a) and effort expectancy (b) of e-wallets among Malaysians.

H3: Discomfort negatively influences performance expectancy (a) and effort expectancy (b) of e-wallets among Malaysians.

H4: Insecurity negatively influences performance expectancy (a) and effort expectancy (b) of e-wallets among Malaysians.

Extant research has applied UTAUT2 to explain user adoption of e-wallets (Tang, Lai, Law, Liew, & Phua, 2014), mobile payments (Hussain, Mollik, Johns, & Rahman, 2019; Kalinic, Marinkovic, Molinillo, & Liébana-Cabanillas, 2019; Liébana-Cabanillas, Molinillo, & Ruiz-Montañez, 2019; Morosan & DeFranco, 2016; Oliveira et al., 2016; Sivathanu, 2019), mobile banking (Alalwan et al., 2017), and mobile financial services (Rahman et al., 2017). The construct “habit” was eliminated as one should have rich experience in using such technology in order to allow examination of the “habit” role (Alalwan, Dwivedi, & Rana, 2017; Oliveira, Thomas, Baptista, & Campos, 2016). Therefore, the following hypotheses were derived:

H5-H10: Performance expectancy (H5), effort expectancy (H6), facilitating conditions (H7), social influence (H8), hedonic motivation (H9) and price value (H10) positively influences behavioural intention to adopt e-wallets among Malaysians.

H11: Behavioral intention to adopt positively influences e-wallet adoption among Malaysians.

Respondents’ profile

IBM SPSS Statistics Version 22.0 was utilized to assess the demographic details such as gender, age, employment status and highest level of academic qualification of the sample respondents and their e-wallet usage status. It was found that the responses were made up of 120 male respondents and 189 female respondents. 74.4% of the respondents are from 18 – 34 years old range. With respect to employment status, 70.9% of them were employed full time. Among all the valid responses, 27.5% of them are degree holders and 21.7% of them qualified at “Sijil Pelajaran Malaysia or also known as SPM or below. It is worth noting that ‘Sijil Pelajaran Malaysia’ is equivalent to O-level certificate.

Based on the responses on the e-wallet usage status, Touch n’ Go e-wallet was found to be the most popular e-wallet with nearly 29.4% reporting using it. Majority of the respondents use e-wallet for transportation transaction (24.4%) and food and beverage transaction (21.4%). 35.9% of the respondents reported having one to six months usage experience. In terms of the frequency of e-wallet usage, 17.5% of the respondents were light users while 17.5% of the respondents were heavy users who used e-wallet more than eight times a month.

Measurement model

To estimate and test the causal relations, partial least squares structural equation modelling (PLS-SEM) was considered because of its appropriateness in prediction purpose, exploratory study, and complex structural models (Hair, Ringle, & Sarstedt, 2011).

The reliability and validity of the constructs were first assessed. Based on the evaluation using SmartPLS, the outer loadings of all items above 0.708, except for INS1 (0.178), INS2 (0.388), and INS3 (0.553) of insecurity. The Average Variance Extracted (AVE) of insecurity was 0.329 which is under the acceptable value of 0.5. Hence, the indicator with the lowest loading, INS1 was deleted and the analysis was reconducted. The results in Table 1 showed that all the remaining indicators of insecurity achieved a satisfactory level for both outer loadings and AVE after removing INS1. The Composite Reliability (CR) values were also found to be above 0.867, indicating that the recommended CR value of 0.60 for exploratory research by Hair, Hult, Ringle, & Sarstedt (2016) was met.

The discriminant validity was analysed. It was found that all the Heterotrait-Monotrait ratios (HTMT) were found to be lower than 0.85, indicating the criteria of Kline (011) is fulfilled. Overall, the criteria of reliability and validity had been achieved at this stage of evaluation.

Structural model

Before testing the hypotheses, the lateral collinearity issues were examined. All the Variance Inflation Factor (VIF) values were found to be below 2.503, suggesting that the issue of lateral collinearity is not a concern for this study.

In the path coefficients’ significance testing, bootstrapping was used to calculate the t-values with 5000 subsamples as the higher the number of subsamples, the higher the accuracy of the results (Hair et al., 2016). As shown in Table 2, all the relationships were found to have t-values of at least 1.617, and thus, significant at 0.10 level of significance, except for H4a and H4b. In the coefficient of determination (R²) assessment, it was found that all the R² values are above the threshold value of 0.26, indicating that the model is substantial.

Next, the effect sizes (f²) of the constructs are evaluated. The value of 0.02, 0.15, and 0.35 signify small, medium, and large effects (Cohen, 1988). Based on the results, it was found that optimism had a medium impact on performance expectancy. The effect size values of performance expectancy, facilitating conditions, social influence, and price value indicated small effects on behavioural intention to adopt. The remaining constructs did not exhibit adequate effects on behavioural intention to adopt.

Predictive relevance was also assessed to predict the visible indicator of the constructs in the model which can be evaluated through Q² value (Stone, 1974). The results showed that all the Q² values are higher than 0, implying that the model possesses a predictive relevance.

Underlying data

Figshare: E_wallet_adoption.csv, https://doi.org/10.6084/m9.figshare.14871048.v1 (Leong et al., 2021).

The project contains the following underlying data:

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).