3.4 Service Consumption Perspective: Technology Adoption
3.4.4 Conceptual Model for m-Government Service Adoption
3.4.4.2 The Unified Model of Technology Adoption for Mobile Enabled Services
Services (UMTAMES) as the conceptual framework to guide the identification of factors influencing citizens’ adoption of m-government services in Tanzania. UMTAMES comprises eight independent variables, namely Performance Expectancy (PE), Self- Efficacy (ES), Hedonic Value (HV), Attitudinal Influences (AI), Subjective Norms (SN), Technology Influences (TI), Facilitating Conditions (FC) and Financial Influences (FI).
Also, Behavioral Intention to use (BI) as an intermediary variable, and Use Behavior (USE) as a dependent variable.
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UMTAMES aligns with the assumptions of UTAUT2; that is, using PE, SE, HV, AI, SN, FC, FI as moderated by age, gender and experience in predicting citizen’s behaviour intention and use behaviour for m-government services. However, the adapted variables from UTAUT2 are modified to capture a wider scope by applying knowledge from the technology use and gratification domain and the technology domestication domain.
Moreover, UMTAMES incorporates an additional variable, TI, in predicting citizens’ BI, which significantly influences USE. According to the EOCD/ITU (2011), technology itself, specifically mobile and wireless technologies, significantly affects citizens’
decisions to adopt the resulting services. The discussion below presents the UMTAMES variables, the corresponding measurements and justifications for each variable.
PE reflects the instrumentality of technology to achieve efficiency and effectiveness in accomplishing a task (Venkatesh, Thong & Xu, 2012). PE has a significant positive effect on behavioural intention (BI) to adopt technology (Venkatesh et al., 2003; Venkatesh, Thong & Xu, 2012). Similarly, UMTAMES assumes a significant effect of PE on BI;
however, the definition of PE is enriched further to reflect the m-government service context. According to Venkatesh, Thong & Xu (2012), the core motive for any technological innovation is how instrumental it is in fulfilling one’s goals or tasks, that is, its utilitarian value. PE needs to include other measures of performance in a wireless and mobile technology context. By employing knowledge from the technology use and gratification domain, PE is expanded to include mobility, flexibility, accessibility, relaxation, and security, as identified by Pedersen et al. (2002).
HV reflects the enjoyment or gratification derived from using technology (Venkatesh, Thong & Xu, 2012). Perdersen et al. (2002), and later, Venkatesh, Thong & Xu (2012), affirm that for everyday m-government services, utilitarian values become less important compared to the derived enjoyment from use. HV, although derived motives, not primarily intended by m-government services, has a positive and significant effect on behavioural intention to use (Pedersen et al., 2002; Thong, Hong & Tam, 2006;
Venkatesh, Thong & Xu, 2012). Therefore, it is essential to evaluate the derived enjoyment from the mobility effect of m-government services.
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SE signifies users’ perceptions of their control to use technology, that is, effort and time expended affects ones’ decision to use technology (Susanto & Goodwin, 2011;
Venkatesh, Thong & Xu, 2012). Venkatesh, Thong & Xu (2012) established a significant positive effect of SE on behavioral intention. SE relates to user’s behavioral change concerning judgment of their capabilities to execute the tasks (Pellas, 2014; Joyce &
Kirakowski, 2015). Several works of literature have substantiated that high self-efficacy motivates adoption (Cheng & Tsai, 2011; Tseng & Tsai, 2010).
AI reflects the outlook or formed opinion of technology (Pellas, 2014). AI reflects the underlying triggers for a particular habit or pattern of behavior. While UTAUT2 evaluates the resulting behavior, this study suggests an evaluation of the motives behind the habit. Self-perception of self-competence, a component within self-efficacy, is said to influence attitude towards technology and hence its adoption (Harsha, 2011; Pellas, 2014). Attitude leads to the development of habit; therefore, developing positive attitudes towards technology encourages its adoption. Similar to Pedersen et al. (2002), age, gender, and experience moderates the effect of AI on BI.
SN, similar to Venkatesh, Thong & Xu’s (2012) social influence in UTAUT2, reflects the influence of other people on consumers’ adoption decisions. SN is said to significantly influence behavioral intention toward technology adoption (Pedersen et al., 2002;
Venkatesh & Bala, 2008; Venkatesh et al., 2003; Venkatesh, Thong & Xu, 2012). To ensure an m-government service context, SN is differentiated from social influence;
while social influence focuses on the influence of opinions and experiences of others in shaping one’s adoption decisions, SN includes both an individual or self- (internal) assessment and the society’s (external) assessment of the experience with technology (Pellas, 2014; Dwivedi et al., 2016). Where there is congruence between interpersonal and social perception of technology, adoption is said to be high (Liu et al., 2011).
TI, in this case, the mobile technology effect, similar to Dholakia & Kshetri (2004) and the OECD/ITU (2011), is postulated to influence the adoption of m-government services significantly. The effect is more magnified in developing countries where poor communication infrastructure and limited skilled personnel and funds prevail (OECD/ITU, 2011).
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The ‘mobility’ by mobile and wireless technology that gives governments the 'anytime,' 'anywhere' and ‘on the move’ edge, certainly adds an appeal to m-government service adoption (Leung & Wei, 2000; Dholakia & Kshetri 2004; Kargin, Basoglu, & Daim, 2009; OECD/ITU, 2011; Al-Lozi & Al-Debei, 2014; Dwivedi et al., 2016). Gratification outcomes concerning mobile and wireless technologies include mobility and immediate access (Leung & Wei, 2000); and more freedom in time and space about interactions achieved using mobile devices (Kargin, Basoglu, & Daim, 2009). In line with Kumar et al. (2013) and Dwivedi et al. (2016), the study suggests using citizen's perceptions of waiting time and the quality of service in terms of compatibility with existing mobile devices, and freedom from space and time, to assess the effect of mobile and wireless technology.
FC reflects the perception of control, availability, and accessibility of sufficient organisational and technological supportive mechanisms and structures to facilitate technology use (Venkatesh & Bala, 2008; Venkatesh, Thong & Xu, 2012). FC, in this study context, adapts all-inclusive behavioral control influences that include all supporting structures and mechanisms to facilitate use extending beyond suppliers’
capabilities. Organisational supportive structures range from communication infrastructure (technology, policy, and regulations) to security issues (Venkatesh, Thong
& Xu, 2012; Salvoldelli Codagnone & Misuraca, 2014). Thus, for m-government services to be adopted, other supportive structures need to be in place; for instance, mechanisms that assure information quality, availability and confidentiality must be in place for citizens’ trust to develop and thus adopt m-government services (Susanto &
Goodwin, 2011; Salvoldelli Codagnone & Misuraca, 2014). However, according to Tanzania regulatory authority January to March quarterly report (2020) the mobile subscription in Tanzania as of January 2020 stood at 44.13 million subscribers, which is 75% of the total population. This indicates that facilitating conditions have less influence on Tanzanians’ attitudes to acquire or use mobile services. Therefore, FC is observed in terms of both behavior control indicators and also trust and security indicators.
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Figure 3.2: Unified Model of Technology Adoption for Mobile Enable Services (UMTAMES)
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FI entails the implications of the cost incurred by a consumer in accessing m-government services. FI differentiates consumer adoption from organisational setting’s adoption and also m- government service adoption from other e-government service adoption, since m-government services apply the pay-per-transaction costing (EOCD/ITU, 2011; Teo et al., 2012). FI reflects the tradeoff between perceived benefits and the monetary value that consumers are willing to spend on the services (Venkatesh, Thong & Xu, 2012). Therefore, in this study, FI is considered as the effect of the perception of price value and pricing strategy for m-government services. However, the FI effect is moderated by gender and age, especially in developing countries where there is a disparity in accessing disposable income (Venkatesh, Thong & Xu, 2012).